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Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Open accessAbstract
Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper develops a Bayesian Spatial Following model that infers Twitter users’ and political actors’ latent ideology from whom they follow, under a homophily assumption. Applied to the US and five European countries, the estimates replicate conventional elite and mass ideology measures and reveal high polarization in 2012 US election retweet networks.
Key findings
- US congressional Twitter ideal points correlate strongly with DW-NOMINATE: ρ = 0.941 in the House and ρ = 0.954 in the Senate; within-party correlations are ρ = 0.546 for Republicans and ρ = 0.610 for Democrats. European party placements are broadly congruent with Chapel Hill Expert Survey estimates, though the UK shows lower accuracy and some extreme parties appear more centrist.
- Mass validation: self-identified conservatives are to the right of independents, who are to the right of liberals; state median Twitter ideal points correlate ρ = 0.876 with Lax and Phillips’s liberal-opinion estimates, higher than with the 2012 Obama vote (ρ = 0.785). Among matched campaign contributors, 90% of 1,058 right-of-median users donated to Republicans and 98% of 1,649 left-of-median users donated to Democrats; correlation with contribution-record ideal points is ρ = 0.80.
- In the 2012 US election application, 65 million tweets mentioning Obama or Romney were sent by 8.8 million users; the analysis focuses on 15 million tweets from the 301,537-user sample. Moderates with ideal points between −0.5 and 0.5 are almost 40% of the sample but sent only 21% of tweets; conservative users (0.5 to 1.5) averaged 118 tweets versus 82 for users between −1.5 and −0.5.
- Retweet interactions are highly polarized: 85% occur among users with similar ideological positions. For Obama retweets, 20% are in the bottom-left quadrant and 69% in the top-right; for Romney retweets, 49% are bottom-left and 31% top-right. Liberal users’ retweets include 18% from moderate/conservative users, while conservative users’ retweets include only 7% from liberal/moderate users.
Methods
- The Bayesian Spatial Following model treats ideology as latent and models the probability that user i follows political actor j as logit⁻¹(α_j + β_i − ||θ_i − φ_j||²), where θ_i is the user’s ideal point, φ_j is the actor’s ideal point, α_j is actor popularity, and β_i is user political interest. Estimation uses hierarchical priors, a No-U-Turn/Hamiltonian Monte Carlo first stage and a random-walk Metropolis-Hastings second stage, implemented in Stan and R.
- Data cover the US, UK, Spain, Germany, Italy, and the Netherlands. Target political actors with more than 5,000 followers in the US or 2,000 elsewhere number m = 318 US, 244 UK, 298 Spain, 214 Italy, 273 Germany, and 118 Netherlands. After filtering inactive, spam, out-of-country, and low-following accounts, final samples are n = 301,537 US, 135,015 UK, 123,846 Spain, 150,143 Italy, 49,142 Germany, and 96,624 Netherlands; the US sample is further restricted to accounts tweeting at least three times about Obama or Romney in the three months before the 2012 election.
- Validation compares Twitter estimates with DW-NOMINATE, Chapel Hill Expert Survey placements, self-identified ideology, Lax and Phillips’s state opinion estimates, campaign contribution records, and Ohio voter registration records. The polarization application collects all public tweets mentioning Obama or Romney from August 15 to Election Day, totaling over 65 million tweets by 8.8 million users, and analyzes the 15 million tweets sent by the 301,537-user sample.
Limitations
- Paper-stated: Twitter users are not a representative sample of the voting-age population; the sample is self-selected, more educated and politically interested, and restricted to users following at least three political accounts, so inferences do not represent all Twitter users. The paper argues this is not critical for estimating politicians’ ideal points.
- Paper-stated: results across countries are not directly comparable because estimation is performed independently and the resulting dimension lacks a homogeneous cross-country scale. The method as formulated estimates ideology only for individuals who follow at least one political account, and the latent dimension may collapse different dimensions, especially in two-dimensional European party spaces; the UK shows lower accuracy and some extreme parties appear more centrist.
- Paper-stated: the retweet polarization conclusion may be partly driven by the assumption that users are more likely to retweet someone they follow, though the text notes retweets between non-followers are frequent. Evident from the text: country location is inferred from profile fields, with country identified in 90% of cases and US state in 71%; the US sample is further restricted to accounts tweeting about Obama or Romney.
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Claims the paper makes
55 Own result · 27 Own interpretation · 17 No basis given · 13 No full text of the sources · 9 Own method · 8 Sources not found · 6 Partly supported · 4 Supported · 4 Not checked yet · 1 Supported so far · 1 Mixed support · 1 Not supported · 1 Couldn’t confirm · accuracy not yet measured
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“similarly i or j can be multiplied by any nonzero constant, with divided by its square, leaving the model predictions unchanged.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Disputedcites Bafumi et al. 2005 sourceExperimental — accuracy not yet measured
Section 2.2.2 describes multiplicative aliasing, but its compensating scale factor is linear, not squared. It says multiplying the c parameters by a constant while dividing both the a and b parameters by that same constant preserves the likelihood; it does not support dividing a normalizing constant by the constant’s square. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
- Partly supportedcites Londregan 1999 sourceExperimental — accuracy not yet measured
Section 3.2 (“First-Order” Identifying Restrictions; page not provided) supports the need to normalize the scale of legislators’ ideal points in the Ladha and linear-probability models. It does not state that dividing a normalizing constant by the square of any nonzero multiplier leaves predictions unchanged; for the Poole–Rosenthal model, it instead says β can be pinned down. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
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- Disputed
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“As Jackman (2001) shows, choosing starting values that are consistent with the expected direction of the scale (e.g., liberals with À1 and conservatives with þ1) is sufficient to ensure global identification in most cases.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Disputedcites Jackman 2001 sourceExperimental — accuracy not yet measured
Section 4 says that starting the Gibbs sampler with Democrats on the left and Republicans on the right is a practical way to select one posterior mode in most unidimensional roll-call settings. It explicitly distinguishes this from global identification: the posterior retains a mirror-image mode, and the priors ensure local identification, not global identification. (LLM, conf=0.90)
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plaindirect- Asserted: passes — The authors put it forward as true.
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- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“any constant can be added to all the parameters i and j without changing the predictions of the model”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Supportedcites Bafumi et al. 2005 source
Section 2.1, “Additive aliasing,” states that adding the same constant to all ability and difficulty parameters leaves the model’s predictions unchanged. Section 2.1.2 explains that predictions depend on the difference between those parameters. (LLM, conf=0.95)
- Partly supportedcites Londregan 1999 sourceExperimental — accuracy not yet measured
Section 3.2, “First-Order” Identifying Restrictions (page unavailable), treats the location of the ideological scale as arbitrary and normalizes one legislator’s position to zero. But Eq. (10) in Section 2.2 shows that voting depends on the difference between a legislator’s ideal point and a proposal’s midpoint. Adding a constant to legislators’ ideal points alone would change that difference; the prediction is unchanged if the proposal locations shift with them. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The model described by equation ( 1) is unidentified: any constant can be added to all the parameters i and j without changing the predictions of the model; and similarly i or j can be multiplied by any nonzero constant, with divided by its square, leaving the model predictions unchanged.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Supportedcites Bafumi et al. 2005 source
Section 2.1, “Additive aliasing,” explicitly says the model is not identified when written as equation (1). It explains that adding the same constant to the ability and difficulty parameters leaves its predictions unchanged. (LLM, conf=0.95)
- Supportedcites Londregan 1999 source
Section 3, “Issues in Model Identification” (page not provided), states that spatial voting models and their Rasch counterparts are not identified without further restrictions. This supports the attributed point that the model is unidentified; the citing sentence’s parameter transformations explain that point rather than changing the claim attributed to the source. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Multiple studies have observed patterns of homophilic segregation consistent with these models in networks of interactions between Twitter users (Wu et al. 2011; Conover et al. 2012).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Not matchedcites Wu et al. 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Conover et al. 2012 source
The Introduction reports a highly segregated partisan structure in a network of political retweets on Twitter. Section 3.3 further reports that the retweet-network communities are highly politically homogeneous, supporting the claim about homophilic segregation among interacting users. (LLM, conf=0.91)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, note that this second approach does not solve the problem of "reflection invariance": the resulting scale can be reversed (flipped left-to-right) without changing the prediction of the models.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Supportedcites Jackman 2001 source
Section 4 says the standard-normal prior solves scale invariance but does not rule out reversing the orientation of the recovered dimension. It states that multiplying all model parameters by −1 yields an identical fit to the data. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In these cases, a model that is able to locate users' and politicians' positions on two different dimensions would be more appropriate, although it would also imply additional identifying assumptions (Jackman 2001).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
What the sources say
- Supportedcites Jackman 2001 source
Section 4, “Moving to Higher Dimensions: Identification Strategies” (page unavailable), explains that moving from one to two dimensions creates additional observationally equivalent rotations and uses informative priors to identify the dimensions. The paper models legislators rather than users; the citation supports the methodological point about additional identifying assumptions, not a finding about users. (LLM, conf=0.88)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated positions of legislators and political parties replicate conventional measures of ideology.”
- Also stated in: § 1 Introduction · plain
“Their estimated ideal points replicate conventional measures of ideology.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
- Also stated in: § 1 Introduction · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Four features stand out: both distributions are bimodal, liberal citizens represent a majority of the sample population, political actors are more polarized than mass voters, and the distribution of mass ideology has longer tails.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The distribution of ideal points for each group closely resembles what one would expect: conservatives are located to the right of independents, and independents are located to the right of liberals, with some overlap.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Many Twitter users define themselves politically in their profiles.
In the paper
“The correlation between these two variables is ¼ 0:876, and it is higher than the correlation with the proportion of the twoparty vote for Obama in 2012 ( ¼ 0:785), which suggests that the meaning of the emerging dimension in my estimation is closer to ideology than to partisanship.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
What the sources say
- Not checked — no full text of the sourcecites Lax and Phillips 2012 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, the order of the parties seems to be similar to that reported by different studies based on expert surveys for the "left-right" dimension.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Users who registered at least five times more often as Democrats than Republicans since 2000 have substantially more liberal ideal points than users who registered more often as Republican than as Democrats.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Four features stand out: both distributions are bimodal, liberal citizens represent a majority of the sample population, political actors are more polarized than mass voters, and the distribution of mass ideology has longer tails.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean ideal point for registered Republicans is higher (more conservative) than the mean estimated ideal point for registered Democrats.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“This is probably the reason why the model performs well in the US case, where economic and social left-right positions are highly correlated and policy preferences are mostly unidimensional (Poole and Rosenthal 2007).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
hedgedSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“And in fact most registered Republicans have estimated ideal points to the right of the estimated ideal points of most registered Democrats.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The distribution of ideal points for each group closely resembles what one would expect: conservatives are located to the right of independents, and independents are located to the right of liberals, with some overlap.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Many Twitter users define themselves politically in their profiles.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The first part shows that Twitter-based ideal points replicate existing measures of ideology for elites (legislators and political parties) in six different countries.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4 Results and Validation
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The distribution of ideal points for each group closely resembles what one would expect: conservatives are located to the right of independents, and independents are located to the right of liberals, with some overlap.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Many Twitter users define themselves politically in their profiles.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Turning to the results in the five European countries I consider, Fig. 3 shows that Twitter-based estimates of the locations of political elites are congruent with other measures based on surveys of experts.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The correlation between these two variables is ¼ 0:876, and it is higher than the correlation with the proportion of the twoparty vote for Obama in 2012 ( ¼ 0:785), which suggests that the meaning of the emerging dimension in my estimation is closer to ideology than to partisanship.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The distribution of ideal points for each group closely resembles what one would expect: conservatives are located to the right of independents, and independents are located to the right of liberals, with some overlap.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Many Twitter users define themselves politically in their profiles.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we can see, the estimated ideal points are clustered into two different groups that align almost perfectly with party membership.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Four features stand out: both distributions are bimodal, liberal citizens represent a majority of the sample population, political actors are more polarized than mass voters, and the distribution of mass ideology has longer tails.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As in the case of the United States, this set of results shows that my estimation method is able to classify accounts according to the party to which they belong.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As was the case with members of Congress, the resulting estimates show a clear division across members of each party”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The correlation between ideal points estimated using Twitter networks and contribution records (Bonica 2014) is ¼ 0:80.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
- Not checked — no full text of the sourcecites Bonica 2014 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, an extremely high proportion of these users are either inactive, are spam bots, or reside in different countries.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Italy, the two largest parties are also correctly classified on the left (PD) and right (PDL) of the ideological spectrum.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“I find that Twitter-based ideology estimates are good predictors of campaign contributions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all, 90% of the 1058 matched Twitter users on the right of the median voter donated to Republican candidates, whereas 98% of the 1649 matched users on the left donated to Democratic candidates.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, if we examine the most extreme legislators, we find that their Twitter-based estimates also position them among those with the highest and lowest values on the ideological scale.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The pattern that emerges is consistent with previous findings in the literature on elite and mass political polarization in the United States (Fiorina and Abrams 2008).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
What the sources say
- Not checked — no full text of the sourcecites Fiorina and Abrams 2008 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The correlation between these two variables is ¼ 0:876, and it is higher than the correlation with the proportion of the twoparty vote for Obama in 2012 ( ¼ 0:785), which suggests that the meaning of the emerging dimension in my estimation is closer to ideology than to partisanship.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all, 90% of the 1058 matched Twitter users on the right of the median voter donated to Republican candidates, whereas 98% of the 1649 matched users on the left donated to Democratic candidates.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moderate individuals are less active than those with more extreme ideological positions, even if they are a majority of the population: users with ideal points between À0.5 and 0.5 represent almost 40% of the sample considered in the study, but they sent only 21% of the tweets included in the collection.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As I show in Section 4.1, the results from multiparty systems clearly show that this dimension overlaps with the left-right scale.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“I find that the conversation on Twitter is dominated by individuals with strong political views: the distribution of the number of tweets is highly bimodal, with the modes at approximately À1 and þ1-that is, one standard deviation away from the average Twitter user.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although liberal users also present this pattern, they tend to engage more often in conversations all along the ideological spectrum.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, of all the retweets by liberal Twitter users (with ideal points lower than -0.5 in the latent scale), 18% were originally sent by moderate and conservative users (with ideal points higher than -0.5).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the case of conservatives (with ideal points 0.5 or higher), only 7% of their retweets were tweets from liberal or moderate users.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the 2012 US presidential election campaign as a case study, I have shown that public exchanges on Twitter take place predominantly among users with similar viewpoints, and also that right-leaning users form a cluster of highly motivated individuals, who dominate public conversations.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
boostedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 7 shows that cross-ideological interactions are rare, since 85% of retweet interactions take place among Twitter users with similar ideological positions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Users are more likely to retweet someone they follow.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, conservative users are more active: users with ideal points between 0.5 and 1.5 sent an average of 118 tweets, whereas those in the interval between À1.5 and À0.5 sent 82 tweets on average.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A distinct pattern emerges in the tweets mentioning President Obama: conservative Twitter users sent a substantially higher proportion of tweets than their liberal counterparts.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“My results show that this method successfully classifies political actors and ordinary citizens according to their political orientation, with the locations along the ideological scale being verified by comparisons to positions estimated using roll call voting, party manifestos, and expert surveys.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plaincorrelationaln = 6- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“I find that the conversation on Twitter is dominated by individuals with strong political views: the distribution of the number of tweets is highly bimodal, with the modes at approximately À1 and þ1-that is, one standard deviation away from the average Twitter user.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Political polarization is particularly intense among right-leaning Twitter users, as indicated by the darker shading.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the fact that Twitter-based ideal points are highly correlated with DW-NOMINATE scores (commonly thought to capture legislators' ideology), and that state-level estimates are better predictors of survey-based measures of ideology rather than partisanship, also suggest that the estimated dimension is the liberal-conservative scale.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the fact that Twitter-based ideal points are highly correlated with DW-NOMINATE scores (commonly thought to capture legislators' ideology), and that state-level estimates are better predictors of survey-based measures of ideology rather than partisanship, also suggest that the estimated dimension is the liberal-conservative scale.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 7 shows that cross-ideological interactions are rare, since 85% of retweet interactions take place among Twitter users with similar ideological positions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Users are more likely to retweet someone they follow.
In the paper
“This assumption is equivalent to that of spatial voting models (see, e.g., Enelow and Hinich 1984).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Not matchedcites Enelow and Hinich 1984
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most importantly, given the sparse nature of the data (roll call votes or contributions) and its costly collection (survey data), current measurement methods generate ideal points that are essentially static in the short run.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this article, I show that using Twitter networks as a source of information about policy positions has the potential to solve these difficulties.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, the large number of active users on this social networking site can be exploited to estimate highly precise ideal points for politicians, if we consider users as”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
In the paper
“There is also little work on cross-national ideological estimation (Lo, Proksch, and Gschwend 2013).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
What the sources say
- Not matchedcites Lo, Proksch, and Gschwend 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“As McPherson, Smith-Lovin, and Cook (2001) theorize, individuals tend to be embedded in homogeneous networks with regard to many sociodemographic and behavioral traits.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Not checked — no full text of the sourcecites McPherson, Smith-Lovin, and Cook (2001) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“But that is not the case in most European countries, where the policy space is usually conceived as two-dimensional (see, e.g., Benoit and Laver 2006).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
What the sources say
- Not checked — no full text of the sourcecites Benoit and Laver 2006 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and the ideal points for all nonpartisan actors have face validity.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the inferences I make based on our sample would not represent the full set of Twitter users, as I am selecting only those users who follow three or more political accounts.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Maximum-likelihood estimation methods are usually intractable given the large number of parameters involved.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.2 The Statistical Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Precisely because they are more likely to be knowledgeable and interested in politics than the average citizen, examining their online behavior can be highly informative about policy positions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Users who follow three or more political accounts are more likely to be knowledgeable and interested in politics than the average citizen.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The correlation between these two variables is ¼ 0:876, and it is higher than the correlation with the proportion of the twoparty vote for Obama in 2012 ( ¼ 0:785), which suggests that the meaning of the emerging dimension in my estimation is closer to ideology than to partisanship.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“An alternative is to apply a unit variance restriction on the set of ideal points, which in the multilevel setting would be equivalent to giving the i s or j s an informative Nð0; 1Þ prior distribution (Gelman and Hill 2007, 318).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Not matchedcites Gelman and Hill 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As it is formulated in this article, the latent dimension that emerges from the model is the one that captures the largest share of the variation in users' following decisions.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that although this sample of users includes 3.5% of all unique users in the collection of tweets, they sent 23% of all tweets, and thus are a good representation of the political discussion that takes place on Twitter.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This result suggests that most tweets sent during this period were negative.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“I have also validated the results by comparing the estimated ideology of a subset of individual Twitter users with their official party registration records.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A final advantage of Twitter as a source of data is the fact that all types of actors are present on this platform.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“I have applied this measure in six different countries: United States, United Kingdom, Spain, Italy, Germany, and the Netherlands.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plainn = 6- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The statistical model as formulated in this article can be used to estimate the ideology only of individuals who follow at least one political account.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, given the number and heterogeneity of users, some valuable data can be extracted from this source.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“My results show that this method successfully classifies political actors and ordinary citizens according to their political orientation, with the locations along the ideological scale being verified by comparisons to positions estimated using roll call voting, party manifestos, and expert surveys.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results highlight the unexplored potential of Twitter data to generate ideology estimates that could prove useful in exploring political behavior.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“One important reason why the work by Jessee (2009), Shor, Berry, and McCarty (2010), and Bonica (2013b) represents an important contribution to the measurement literature is that ideology estimates for citizens, state legislators, political action committees (PACs), and political candidates were not previously available.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the fact that Twitter-based ideal points are highly correlated with DW-NOMINATE scores (commonly thought to capture legislators' ideology), and that state-level estimates are better predictors of survey-based measures of ideology rather than partisanship, also suggest that the estimated dimension is the liberal-conservative scale.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 39
- It may state two separate claims as one. 40
- The quote only partly states this claim. 1
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These indeterminacies, which are common to item-response theory models, are sometimes called "additive aliasing" and "scaling invariance" (see, e.g., Bafumi et al. 2005; Londregan 1999).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Supportedcites Bafumi et al. 2005 source
Section 2.1 calls the item-response model’s unchanged predictions under a shared shift in ability and difficulty parameters “additive aliasing.” Section 2.2.1 says that adding a discrimination parameter creates “scaling invariance or multiplicative aliasing.” (LLM, conf=0.94)
- Partly supportedcites Londregan 1999 sourceExperimental — accuracy not yet measured
Section 3, “Issues in Model Identification” (page unavailable), says spatial voting models and their psychometric cousins, Rasch models, are not identified without further restrictions. Section 2.2 equates a spatial-model voting condition with one from an educational-testing model. The copy does not identify the indeterminacies as “additive aliasing” and “scaling invariance,” so it supports the shared identification problem but not those specific characterizations. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Given the dynamic nature of social media, its large size, and individuals' finite ability to process incoming information (Oken Hodas and Lerman 2012), we should expect Twitter users to maximize the value of their online experience by choosing to follow political actors who can provide information that can be of higher value to them.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Supportedcites Oken Hodas and Lerman 2012 source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Existing studies on ideal point estimation employ two different strategies to identify the model.One is to apply two linearly independent restrictions on the ideal point parameters, u or f in this case.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Partly supportedcites Londregan 1999 sourceExperimental — accuracy not yet measured
Section 3.2 (“First-Order” Identifying Restrictions; page not provided) describes identifying models by imposing two restrictions on legislators’ preferred-point locations, including setting one location to zero and another to one in the Ladha and linear-probability models. The passages do not describe a unit-variance restriction as a second strategy, so that part of the attributed claim is unsupported. (LLM, conf=0.82)
- Not checked — no full text of the sourcecites Clinton, Jackman, and Rivers 2004 source
- Not matchedcites Gelman and Hill 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“In particular, the usual procedure is to constrain the ideal points of two legislators (liberal and conservative) at arbitrary positions, such as À1 and þ1 (see, e.g., Londregan 1999; Clinton, Jackman, and Rivers 2004).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
What the sources say
- Supportedcites Londregan 1999 source
The passage labeled “Proof of Theorem 1” explicitly gives the example of placing one legislator on the left at −1 and another on the right at 1 to normalize the scale. Section 3.2 also describes fixing the positions of two legislators as identifying restrictions. (LLM, conf=0.93)
- Not checked — no full text of the sourcecites Clinton, Jackman, and Rivers 2004 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The samplers in both stages are run using two chains with as many iterations as necessary to ensure that all ideology parameters have an effective number of simulation draws (Gelman and Rubin 1992) of at least 200.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.4 MCMC Algorithm
What the sources say
- Could not verifycites Gelman and Rubin 1992 sourceExperimental — accuracy not yet measured
The citation is attached to “effective number of simulation draws.” Section 2.2 (p. 5) describes monitoring convergence with potential scale reduction, while Section 4.5 (pp. 12–13) uses 200 iterations in an example; neither passage defines an effective number of draws or recommends a threshold of 200. Searches for “effective sample size” and “number of effectively independent simulations” found no passage establishing that metric. (LLM, conf=0.76)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“As Conover et al. (2012) argue, the topology of the network of right-leaning Twitter users facilitates the rapid and broad dissemination of political information, and increases their potential to manipulate the public agenda.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
- Partly supportedcites Conover et al. 2012 sourceExperimental — accuracy not yet measured
The Introduction and Conclusion state that right-leaning users’ network topology facilitates the rapid and broad dissemination of political information. Section 5.3 also suggests more opportunities for political frame-making, but the paper does not say that this increases their potential to manipulate the public agenda. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Given that a substantial proportion of citizens now rely mostly on the Internet to gather political information, 21 the policy implications of this issue are obvious: how individuals gather political information affects the quality of political representation, the policy-making process, and the stability of the democratic system (Mutz 2002).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
- Partly supportedcites Mutz 2002 sourceExperimental — accuracy not yet measured
The Discussion (page not specified) reports modest consequences of cross-cutting political communication networks for political tolerance and democratic legitimacy. This supports the general idea that political communication matters for democracy, but the paper does not establish effects on the quality of political representation, policy-making, or the stability of the democratic system. (LLM, conf=0.83)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To illustrate a potential use of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines, finding support for the so-called”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
In the paper
“In fact, this figure is an almost exact replication of Fig. 5 in Bafumi and Herron (2010).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
What the sources say
- Not checked — no full text of the sourcecites Bafumi and Herron 2010 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Spain, the Socialist Party (PSOE) is located to the left of the Conservative Party (PP), with the recently created Center Party (UPyD) between them, and the Communist Party to the left of all three, although overlapping with the PSOE.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With few exceptions, all Twitter accounts from the same party are clustered together, and parties with similar ideologies are located in similar positions on the latent ideological dimension.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 2707 and 2462 Twitter users were successfully matched in each case, based on perfect nonduplicated matches of first and last name, and zip code or county.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As I show in the supplementary materials, either of these strategies can be applied in this case to resolve the indeterminacies.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.3 Identification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Four features stand out: both distributions are bimodal, liberal citizens represent a majority of the sample population, political actors are more polarized than mass voters, and the distribution of mass ideology has longer tails.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.2 Validation of Mass Ideology at the Aggregate Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The panel on the right, which classifies voters according to their registration history (the number of times they registered as Democrat minus the number of times they registered as Republican), indicates that this is indeed the case.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The correlation between Twitter-and roll-call-based ideal points is ¼ 0:941 in the House and ¼ 0:954 in the Senate.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the Netherlands, all parties are accurately scaled, with the exception of the Socialist Party (SP) and the right-wing Party for Freedom (PVV), which appear slightly more centrist in my Twitter estimates than in the expert surveys.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results show the lower degree of accuracy in the UK: both the Labor Party and the Liberal Democrats are located to the left of the Conservative Party on average, but the latter is classified as right-wing, almost overlapping with the conservatives, perhaps indicating their status as coalition partners.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Within-party correlations are also relatively high: ¼ 0:546 for Republicans, ¼ 0:610 for Democrats.17”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Germany, the locations of the three right-wing parties (CDU, CSU, and FDP) on the ideological dimension are indistinguishable, which is consistent with the experts' assessments, but Die Linke appears to be more centrist.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Of course, Twitter users are a self-selected minority of the population, but this minority includes virtually all political parties and candidates, public officials, interest groups, media outlets, journalists, and many others for whom we do not necessarily have good measures of ideology.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the 2012 US presidential election campaign as a case study, I have shown that public exchanges on Twitter take place predominantly among users with similar viewpoints, and also that right-leaning users form a cluster of highly motivated individuals, who dominate public conversations.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that although this sample of users includes 3.5% of all unique users in the collection of tweets, they sent 23% of all tweets, and thus are a good representation of the political discussion that takes place on Twitter.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, as I have shown in this article, it is possible to identify the location of most Twitter users, as well as their gender, and to track their behavior over time, which could help recover the representativeness of this sample of individuals.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the analysis below, I focus on the 15 million tweets that were sent by the 301,537 individuals in my sample of politically interested users.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plainn = 301,537- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In all the examples in this article, the correlation between these two sets of estimates is ¼ :999.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moderate individuals are less active than those with more extreme ideological positions, even if they are a majority of the population: users with ideal points between À0.5 and 0.5 represent almost 40% of the sample considered in the study, but they sent only 21% of the tweets included in the collection.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Of course, Twitter users are a self-selected minority of the population, but this minority includes virtually all political parties and candidates, public officials, interest groups, media outlets, journalists, and many others for whom we do not necessarily have good measures of ideology.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, conservative users are more active: users with ideal points between 0.5 and 1.5 sent an average of 118 tweets, whereas those in the interval between À1.5 and À0.5 sent 82 tweets on average.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, given the fast-paced nature of Twitter, it also creates opportunity costs, since it reduces the likelihood of being exposed to other messages, assuming the amount of time a user spends on Twitter is constant.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- The authors state this as an assumption: The amount of time a user spends on Twitter is constant.
In the paper
“However, Twitter is not only an online social network-it is also a news media (Kwak et al. 2010).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Not checked — no full text of the sourcecites Kwak et al. 2010 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, the structure of this network is far from static, which can facilitate the estimation of highly granular dynamic ideal points in real time.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Although obviously less costly than campaign contributions or votes in a legislature, the assumption behind this model is similar in nature to that justifying how donations and roll call votes can be scaled onto a latent ideological dimension (Poole and Rosenthal 2007; Bonica 2014).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
- Not matchedcites Poole and Rosenthal 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Bonica 2014 source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities.”
- Also stated in: § 1 Introduction · plain
“This method represents an additional measurement tool that can be used to estimate ideology, an important quantity of interest in political science, for a larger set of political actors and individuals than any other method before.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Abstract
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- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
- Also stated in: § 1 Introduction · plain
None of the sources cited for this claim have been checked yet.
In the paper
“the existing literature on the selective exposure theory (Lazarsfeld, Berelson, and Gaudet 1944; Bryant and Miron 2004; Stroud 2008; Iyengar and Hahn 2009) that argues that individuals exhibit a preference for opinion-reinforcing political information and that they systematically avoid opinion challenges.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
What the sources say
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In the paper
“I argue that valid policy positions for ordinary users and political actors can be inferred from the structure of the”
- Also stated in: § 2.1 Assumptions · boosted
“In this article, I demonstrate that valid ideal point estimates of individual Twitter users and political actors with a Twitter account can be derived from the structure of the”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
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- The authors state this as an assumption: Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
- Also stated in: § 2.1 Assumptions · boosted
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the content of the messages users are exposed to as a result of their following decisions challenges their political views, it can create cognitive dissonance.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.1 Assumptions
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In the paper
“Third, it is possible to link Twitter profiles to other data through name identification, which provides interesting ways to examine differences between private and public political behavior.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
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In the paper
“When the analysis also includes voters, it is done at the expense of strong”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 1 Introduction
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A key challenge in implementing this method is the choice of the m target Twitter users who are political elites: the set of users with "discriminatory" predictive power such that the decision to follow them (or not) provides information about an individual's ideology.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
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In the paper
“However, this should not affect the inference of politicians' ideal points, since these users can indeed be considered as "authoritative" when it comes to politics.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Users who follow three or more political accounts are more likely to be knowledgeable and interested in politics than the average citizen.
In the paper
“This is probably the reason why the model performs well in the US case, where economic and social left-right positions are highly correlated and policy preferences are mostly unidimensional (Poole and Rosenthal 2007).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
What the sources say
- Not matchedcites Poole and Rosenthal 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that their positions within each cluster are what we would expect based on anecdotal evidence.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This is a highly self-selected sample because Twitter users are not a representative sample of the population.14”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“As a result of this transformation, individuals are being increasingly exposed to only information that reinforces their existing views, thus avoiding challenging opinions (Sunstein 2001; Garrett 2009; Iyengar and Hahn 2009).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This procedure is roughly analogous to an expert survey with many respondents where each respondent provides a small amount of information that, when aggregated, results in highly accurate policy estimates.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, samples from the posterior density of each parameter in the model can be obtained using Markov Chain Monte Carlo methods.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.2 The Statistical Model
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In the paper
“Note that each of these parameters can be estimated individually because I assume local independence, conditional on the j parameters, and therefore multi-core processors can be used to run multiple samplers simultaneously and dramatically increase computation speed.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.4 MCMC Algorithm
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- The authors state this as an assumption: Individual decisions to follow are independent across users n and m, conditional on the estimated parameters (local independence).
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This modelfitting strategy appears to be quite robust, and my results are largely insensitive to the choice of priors and initial values.foot_3”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.4 MCMC Algorithm
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“A recurring theme in the literature on the Internet and politics is how the increasing amount and heterogeneity of political information citizens have access to affects their political views (Farrell 2012).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
- Not checked — no full text of the sourcecites Farrell 2012 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To improve the efficiency of this procedure, I use a Hamiltonian Monte Carlo algorithm (Gelman et al. 2013) and employ a hierarchical setup that considers each of the four sets of parameters as draws from four common population distributions:”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.2 The Statistical Model
What the sources say
- Not checked — no full text of the sourcecites Gelman et al. 2013 source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“This is probably the reason why the model performs well in the US case, where economic and social left-right positions are highly correlated and policy preferences are mostly unidimensional (Poole and Rosenthal 2007).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
What the sources say
- Not matchedcites Poole and Rosenthal 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set of users that are considered will determine the interpretation of the latent scale where ideal points are located and, for this reason, it is important to include identifiable figures with extreme ideological positions, beyond just partisans.8”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimation method I propose in this article can be applied to any country where a high number of citizens are discussing politics on Twitter.9”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 2.5 Discussion
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It is important to note that the results for different countries are not directly comparable, as the estimation was performed independently, and the resulting dimension does not have a homogeneous scale across countries.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.1 Replication of Legislators' and Parties' Ideal Points
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Samples from the i parameters in the second stage can be compared with those obtained for the random sample in the first stage to ensure that there were no errors in the estimation.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the United States, where partisanship and ideology are highly correlated, it is not as clear.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This new source of information has the potential to generate much value added to explanatory models of political behavior at a low cost.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Bafumi and Herron (2010) estimate ideology for the mass public using the Cooperative Congressional Election Study (N ¼ 33,000), which included questions asking respondents to take positions on roll call votes as if they were members of Congress.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
What the sources say
- Not checked — no full text of the sourcecites Bafumi and Herron 2010 source
plainn = 33,000- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the model is agnostic regarding the interpretation of the latent dimension, which will depend on the set of m political actors that are considered.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To sum up, the picture that emerges points toward a high degree of polarization, which is driven predominantly by conservative Twitter users.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- The authors state this as an assumption: Users are more likely to retweet someone they follow.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that this should not affect the estimation as long as these two dimensions are orthogonal to each other, in which case the estimated latent space would correspond to the dimension that explains a larger proportion of the variance.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > Funding
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“In all, 4.3 million US citizens made a political contribution in the 2012 electoral cycle (Bonica 2013a), but 15% of online adults (almost forty million citizens) are active on Twitter.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
What the sources say
- Not matchedcites Bonica 2013a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“In all, 4.3 million US citizens made a political contribution in the 2012 electoral cycle (Bonica 2013a), but 15% of online adults (almost forty million citizens) are active on Twitter.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 6 Conclusions
What the sources say
- Not matchedcites Pew Research Center 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“By using the method developed in this article, I am able to compute ideal points for a far larger sample of users than any of the previous studies, which now includes most active Twitter users in the United States, and with a much higher degree of precision.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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It may state two separate claims as one.
In the paper
“Given that a substantial proportion of citizens now rely mostly on the Internet to gather political information, 21 the policy implications of this issue are obvious: how individuals gather political information affects the quality of political representation, the policy-making process, and the stability of the democratic system (Mutz 2002).”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
- Not matchedcites Pew Research Center 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This generates a so-called echo-chamber environment that fosters social extremism and political polarization.”
§ Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data > 4.3 Validation of Mass Ideology at the Individual Level
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The authors state this as an assumption.
Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.
“The key assumption of this model is that Twitter users prefer to follow politicians whose positions on the latent ideological dimension are similar to theirs.”
The paper relies on this without support.
Most studies estimate ideal points for legislators only.
“However, most studies estimate ideal points for legislators only.”
The paper relies on this without support.
Citizens who discuss politics on Twitter are more likely to be educated and politically interested, and that makes them a particularly useful source of information about elites' ideology.
“Citizens who discuss politics on Twitter are more likely to be educated and politically interested, and that makes them a particularly useful source of information about elites' ideology.”
The authors state this as an assumption.
Following decisions are costly signals that provide information about Twitter users' perceptions of both their ideological location and that of political accounts.
“I consider following decisions to be costly signals about users' perceptions of both their ideological location and that of political accounts.”
The paper relies on this without support.
Virtually every legislator, political party, and candidate in developed democracies has an active Twitter account.
“Virtually every legislator, political party, and candidate in developed democracies has an active Twitter account.”
The paper relies on this without support.
Twitter users are embedded in a common social network.
“Most importantly, they are embedded in a common social network.”
The authors state this as an assumption.
The amount of time a user spends on Twitter is constant.
“assuming the amount of time a user spends on Twitter is constant”
The authors state this as an assumption.
Individual decisions to follow are independent across users n and m, conditional on the estimated parameters (local independence).
“Assuming local independence (individual decisions to follow are independent across users n and m, conditional on the estimated parameters), the likelihood function to maximize this model is as follows:”
The authors state this as an assumption.
The decision to follow a political actor is a function of the squared Euclidean distance in the latent ideological dimension between the user and the actor.
“For the reasons explained above, I expect this decision to be a function of the squared Euclidean distance in the latent ideological dimension 5 between user i and j: À jj i À j jj 2”
The paper relies on this without support.
Differences in the number of political accounts each user follows are related to the overall number of Twitter users they follow or their overall level of interest in politics.
“Similarly, this parameter accounts for the differences in the number of political accounts each user i decides to follow, which could be related to the overall number of Twitter users they follow, or their overall level of interest in politics.”
The paper relies on this without support.
Homophilic networks can be based not only on political traits, but also on other personal characteristics.
“However, "homophilic" networks can be based not only on political traits, but also on other personal characteristics.”
The paper relies on this without support.
High-quality ideology measures are available for a subset of all Twitter users in the US, UK, Spain, Germany, Italy, and the Netherlands.
“However, in order to test the validity of the estimated parameters, I will focus on six countries where high-quality ideology measures are available for a subset of all Twitter users: the US, the UK, Spain, Germany, Italy, and the Netherlands.”
The paper relies on this without support.
Users who follow three or more political accounts are more likely to be knowledgeable and interested in politics than the average citizen.
“Precisely because they are more likely to be knowledgeable and interested in politics than the average citizen”
“these users can indeed be considered as "authoritative" when it comes to politics.”
The paper relies on this without support.
Many Twitter users define themselves politically in their profiles.
“Here, I exploit the fact that many Twitter users define themselves politically in their profiles.”
The authors state this as an assumption.
Users are more likely to retweet someone they follow.
“It may be argued that this conclusion is driven at least in part by the key assumption behind the method to estimate ideology, since users are more likely to retweet someone they follow.”
The paper relies on this without support.
As citizens spend a greater proportion of their time online, most of their political behavior also takes place through social networking platforms.
“As citizens spend a greater proportion of their time online, most of their political behavior also takes place through social networking platforms.”
The paper relies on this without support.
Most existing work relies on crude measures of political preferences.
“Being able to estimate covariates of interest represents an important step in the study of such behavior, since most existing work relies on crude measures of political preferences.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“We employ the methodology of Barberá (2015) to measure users' political orientation.”
From Cross-Partisan Interactions on Twitter (2025) · cites this paper as Barberá 2015
The cited source says what the claim attributes to it.
In the citing paper
“Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs (Barberá, 2015;Conover et al., 2011).”
From Credibility of climate change denial in social media (2019) · cites this paper as Barberá, 2015
Citations
Cites
Equation of State Calculations by Fast Computing Machines
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A., & Hill, J. (2006) · doi:10.1017/cbo9780511790942 · Cited by 13,597
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
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Eaton, B. C., & Schelling, T. C. (1981) · Canadian Journal of Economics/Revue canadienne d économique · doi:10.2307/134812 · Cited by 3,801
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Earthquake shakes Twitter users
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Research works printed but not linked: 2
- It may be argued that this conclusion is driven at least in part by the key assumption behind the method to estimate ideology, since users are more likely to retweet someone they follow. However, note that retweets between users who do not follow each other are indeed frequent. For example, of the 460,000 users who retweeted at least one of Barack Obama's tweets between August 15, 2012, and November 6, 2012, 152,000 (33%) did not follow him as of November 5, 2012. In fact, 21.2 of his 21.5 million followers at the time did not retweet any of his tweets during this period.
- King A. , Orlando F. , and Sparks D. B. 2011. Ideological extremity and primary success: A social network approach. Chicago, Illinois: Paper presented at the 2011 MPSA Conference.
Cited by
Sorted by citations
Exposure to opposing views on social media can increase political polarization
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804840115 · Cited by 1,863
“These accounts were identified via a network-sampling technique that assumes those with similar political ideologies are more likely to follow each other on Twitter than those with opposing political ideologies (44).”
BackgroundTweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015) · Psychological Science · doi:10.1177/0956797615594620 · Cited by 1,472
“Latent space models are usually estimated using Markov-chain Monte Carlo methods, because standard maximum likelihood approaches are intractable for medium-to-large networks (Barberá, 2015; Shortreed, Handcock, & Hoff, 2006).”
BackgroundMethodInfluentialSelf-citationEmotion shapes the diffusion of moralized content in social networks
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618923114 · Cited by 1,580
“Our moods, thoughts, and actions are shaped by the entire network of individuals with whom we share direct and indirect relationships (5).”
BackgroundMethodInfluentialArtificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707
“This approach is common in computational social science, in which Quants are used to estimate proxies for concepts that are not directly observable 132,133 , such as people’s political ideology 134 or emotional expressions 135,136 .”
Boutyline, A., & Willer, R. (2017) · Political Psychology · doi:10.1111/pops.12337 · Cited by 491
“The parallels between Twitter and offline electoral politics are also illustrated by Barber a (2015), who shows that the ideological positions of members of the 112th Congress can be estimated solely from Twitter ties among their followers.”
BackgroundMethodInfluential
Metadata
- Authors
- Barberá, P.
- Affiliations
- New York University (US)
- Corresponding author
- Barberá, Pablo
- ORCID
- Barberá, Pablo
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2015
- Volume
- 23
- Issue
- 1
- Pages
- 76-91
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2161834943
- Semantic Scholar
- 119306396df0ec0c751aca034c11a36c45338dde
- Cited by
- 982
- References
- 86
- Citations by year
- 2026: 46 · 2025: 89 · 2024: 80 · 2023: 100 · 2022: 121 · 2021: 87 · 2020: 133 · 2019: 81 · 2018: 74 · 2017: 82 · 2016: 48 · 2015: 26
- FWCI
- 165.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Political Analysis (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1248077)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- Twitter, ideal points, political debate, legislators, political parties, party registration, 2012 presidential election, online political behavior, latent variable models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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