The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index; 18 of the 56 sources its claims cite were checked for support.
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
18 of 56 cited sources checked · 9 supported · 2 flagged · 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Checked by a reasoning model — may be wrong.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Many Twitter users define themselves politically in their profiles.
- 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)
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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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
“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
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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
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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
“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
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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
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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
Strength:plain· not yet calibratedn = 301,537Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- 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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one. The quote only partly states this claim.
- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. 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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
“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
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“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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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, 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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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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 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 33,000Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
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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
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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.”
BackgroundMethodInfluentialHow Social Media Facilitates Political Protest: Information, Motivation, and Social Networks
Jost, J. T., Barberá, P., Bonneau, R., Langer, M., Metzger, M., Nagler, J., Sterling, J., & Tucker, J. A. (2018) · Political Psychology · doi:10.1111/pops.12478 · Cited by 492
Self-citationComputer-Assisted Text Analysis for Comparative Politics
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“…Roberts (2013) studied the focus of censorship in social media in China; Jamal et al. (n.d.) studied anti-Americanism in Arabic-language Twitter posts, and Barberá (2012) used Twitter posts to scale citizen liberal-conservative ideal points across the United States and several European countries.”
BackgroundCOVID-19 Government Response Event Dataset (CoronaNet v.1.0)
Cheng, C. I.-F., Barceló, J., Hartnett, A. S., Kubinec, R., & Messerschmidt, L. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0909-7 · Cited by 465
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“Using information on voter registration history, Barberá (2013) matches a sample of voters from Ohio, a state that requires party registration to vote in a primary election, to their Twitter accounts and finds a strong correlation between party registration and the parties these voters follow on…”
BackgroundMellon, J., & Prosser, C. (2017) · Research & Politics · doi:10.1177/2053168017720008 · Cited by 396
“, 2015), and study online deliberation (Larsson and Moe, 2011), political mobilization (Carlisle and Patton, 2013; Vissers and Stolle, 2014), and political ideology (Barbera, 2014; Bond and Messing, 2015).”
BackgroundUnderstanding the Political Representativeness of Twitter Users
Barberá, P., & Rivero, G. (2014) · Social Science Computer Review · doi:10.1177/0894439314558836 · Cited by 394
“Party identification was recovered applying an item-response model to the network of friends of the users (Barberá, 2014).”
MethodInfluentialSelf-citationHow social learning amplifies moral outrage expression in online social networks
Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021) · Science Advances · doi:10.1126/sciadv.abe5641 · Cited by 375
“We used these data to estimate the ideological extremity of each ego in our dataset ( 49 ), as well as all of each ego’s friends and followers, yielding estimates of each ego’s network-level ideological extremity (see Fig.”
MethodFrenemies: How Social Media Polarizes America
Settle, J. E. (2018) · doi:10.1017/9781108560573 · Cited by 377
Growing polarization around climate change on social media
Falkenberg, M., Galeazzi, A., Torricelli, M., Marco, N. D., Larosa, F., Sas, M. I., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01527-x · Cited by 459
“The latent ideology estimation was developed in [22, 23] and adapted for exploiting retweet interactions in [24].”
BackgroundKitchens, B., Johnson, S. L., & Gray, P. (2020) · MIS Quarterly · doi:10.25300/misq/2020/16371 · Cited by 376
“…groups” (Shore et al. 2018, p. 850), with “political fragmentation and social polarization” (Garrett 2009, p. 278), and with “segregation by interest or opinion [that] will … increase political polarization” (Dubois and Blank 2018, pp. 1-2) and “foster social extremism” (Barberá 2015, p. 86).”
BackgroundPolitical Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
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“We adopt the latent space model proposed by Barberá et al. [7] to estimate a user polarity score.”
BackgroundResultInfluentialSpontaneous Collective Action: Peripheral Mobilization During the Arab Spring
STEINERT-THRELKELD, Z. (2017) · American Political Science Review · doi:10.1017/s0003055416000769 · Cited by 345
Media Influences on Social Outcomes: The Impact of MTV’s 16 and Pregnant on Teen Childbearing
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“For example, new models of political partisanship are being generated by Federal Election Commission data, Twitter, press releases, and floor speeches (Barberá 2015, Bonica 2014, Gentzkow et al. 2016, Tsur et al. 2015).”
MethodLarge-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges
Wilkerson, J., & Casas, A. (2017) · Annual Review of Political Science · doi:10.1146/annurev-polisci-052615-025542 · Cited by 300
“Barbera (2015) uses Twitter data and information about their followers to estimate the ideological positions of politicians, parties, and individual citizens.”
BackgroundHow Social Media Is Changing Conflict
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“One possible mechanism for this is that social media platforms can magnify the connections between individuals who share what Granovetter (1973), in a classic article, calls “weak ties” (Barberá, 2015b).”
BackgroundMethodInfluentialIs the Left-Right Scale a Valid Measure of Ideology?
Bauer, P. C., Barberá, P., Ackermann, K., & Venetz, A. (2016) · Political Behavior · doi:10.1007/s11109-016-9368-2 · Cited by 306
Self-citationTwitter Style: An Analysis of How House Candidates Used Twitter in Their 2012 Campaigns
Evans, H. K., Cordova, V., & Sipole, S. L. (2014) · PS Political Science & Politics · doi:10.1017/s1049096514000389 · Cited by 259
“Some research suggests, however, that followers of candidates are those already aligned with their political platform (Barbera 2013), which means that many of these attack tweets and mobilization tweets are not being seen by those with few existing attitudes who would be more aff ected by the…”
BackgroundVaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J. T., Nagler, J., & Tucker, J. A. (2015) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12108 · Cited by 256
“model” described in Barberá (2014), which estimates ideology based on the political actors and”
BackgroundMethodSelf-citationPolarization: What Everyone Needs to Know®
McCarty, N. (2019) · doi:10.1093/wentk/9780190867782.001.0001 · Cited by 280
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The Volatility of the Discourse on Refugees in Germany
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“One of the popular methods of including the impact of social media are analyses based on Twitter data (see e.g., Barber a, 2015).”
MethodPolitical Socialization and the Making of Citizens
Neundorf, A., & Smets, K. (2017) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199935307.013.98 · Cited by 216
Social media and political communication in the 2014 elections to the European Parliament
Nulty, P., Theocharis, Y., Popa, S. A., Parnet, O., & Benoit, K. R. (2016) · Electoral Studies · doi:10.1016/j.electstud.2016.04.014 · Cited by 229
“(Barber a, 2015; Barber a et al., 2015).8 An interesting and contrary pattern emerges to that shown above for the probability of having a Twitter account.”
BackgroundBond, R. M., & Messing, S. (2015) · American Political Science Review · doi:10.1017/s0003055414000525 · Cited by 211
A Survey of Sentiment Analysis from Social Media Data
Chakraborty, K., Bhattacharyya, S., & Bag, R. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2956957 · Cited by 207
Approaches to Studying Policy Representation
Broockman, D. (2016) · Legislative Studies Quarterly · doi:10.1111/lsq.12110 · Cited by 200
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“…SNS use promotes network heterogeneity (Lee, Choi, Kim, & Kim, 2014) and the formation of weak ties, implying that SNSs expand our “political horizons” (Barberá, 2013), if these weak ties are fragile and are broken in the face 16 Journal of Communication (2015) © 2015 International Communication…”
BackgroundZhang, H., & Pan, J. (2019) · Sociological Methodology · doi:10.1177/0081175019860244 · Cited by 191
“Unmediated Channel From the Arab Spring to Occupy to the MeToo movement, social media has become an important venue for protesters to speak out (Barberá 2015; Budak and Watts 2015; González-Bailón et al. 2011; SteinertThrelkeld 2017; Steinert-Threlkeld et al. 2015).”
BackgroundAffective polarization and the salience of elections
Hernández, E., Anduiza, E., & Rico, G. (2020) · Electoral Studies · doi:10.1016/j.electstud.2020.102203 · Cited by 209
“…hostility in some countries, such as the US, consider this a consequence of complex social processes such as the expansion of social media (Barberá, 2015), increasing media incivility (Mutz, 2015), negative campaigning (Iyengar, Sood, & Lelkes, 2012) or growing inequality (Gidron, Adams,…”
BackgroundBright, J. (2018) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmx002 · Cited by 207
“…& Kane, 2008; Lawrence, Sides, & Farrell, 2010) and most recently on social media (Aragón, Kappler, Kaltenbrunner, Laniado, & Volkovich, 2013; Barberá, 2014; Colleoni, Rozza, & Arvidsson, 2014; Conover, Gonçalves, Ratkiewicz, Flammini, & Menczer, 2011; Conover, Gonçalves, Flammini, &…”
BackgroundResultInfluentialUnsupervised User Stance Detection on Twitter
Darwish, K. M., Stefanov, P., Aupetit, M. J. M., & Nakov, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7286 · Cited by 169
“…recent interest in stance detection involving the determination of a person’s position on an issue including the deduction of political preferences (Barber´a 2015; Barber´a and Rivero 2014; Borge-Holthoefer et al. 2015; Cohen and Ruths 2013; Colleoni, Rozza, and Arvidsson 2014; Conover et al.…”
BackgroundMethodJungherr, A. M., Rivero, G., & Gayo-Avello, D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108297820 · Cited by 227
Using Sentiment Analysis to Monitor Electoral Campaigns
Cerón, A., Curini, L., & Iacus, S. M. (2014) · Social Science Computer Review · doi:10.1177/0894439314521983 · Cited by 158
“…to assess the popularity of politicians (Gloor, Krauss, Nann, Fischbach, & Schoder, 2009), to track the political alignment of social media users (Barbera´, 2012; Conover, Goncalves, Ratkiewicz, Flammini, & Menczer, 2011), and to compare citizens’ political preferences expressed online with…”
BackgroundTheocharis, Y., Barberá, P., Fazekas, Z., Popa, S. A., & Parnet, O. (2016) · Journal of Communication · doi:10.1111/jcom.12259 · Cited by 237
Self-citationHavey, N. F. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00089-2 · Cited by 152
“Finally, as other researchers have shown, Twitter is not necessarily representative of the larger public and is most actively used by a politically active, educated, and a younger section of the population [25, 48].”
BackgroundMethodThe New Public Address System: Why Do World Leaders Adopt Social Media?
Barberá, P., & Zeitzoff, T. (2017) · International Studies Quarterly · doi:10.1093/isq/sqx047 · Cited by 142
Self-citationCrisis Communications in the Age of Social Media
Hagen, L., Keller, T., Neely, S. R., DePaula, N., & Robert, C. E. (2017) · Social Science Computer Review · doi:10.1177/0894439317721985 · Cited by 144
“These communities can be identified because people are more inclined to communicate with others who hold similar interests (Barberá, 2015; Xu et al., 2015).”
BackgroundJungherr, A. M., Schoen, H., & Jürgens, P. (2015) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12143 · Cited by 135
“…(e.g. Anstead & O’Loughlin, 2015; Kreiss 2014), and the growing use of digital trace data by researchers to draw inferences on political phenomena (e.g. Barberá, 2015; Tumasjan et al., 2010)—illustrate the increasing role of these data in the public construction of social and political reality.”
BackgroundSchöne, J. P., Parkinson, B., & Goldenberg, A. (2021) · Affective Science · doi:10.1007/s42761-021-00057-7 · Cited by 127
“Thus, users who follow a greater number of liberal political figures are more likely to be liberals (after excluding extremely popular politicians such as the president or candidates who are running for office from the count) (Barberá, 2015).”
BackgroundVan Bavel, J. J., Robertson, C. E., del Rosario, K., Rasmussen, J., & Rathje, S. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022123-110258 · Cited by 121
Does Social Media Influence Conflict? Evidence from the 2012 Gaza Conflict
Zeitzoff, T. (2016) · Journal of Conflict Resolution · doi:10.1177/0022002716650925 · Cited by 131
Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01550-8 · Cited by 113
The Challenge of Big Data and Data Science
Brady, H. E. (2019) · Annual Review of Political Science · doi:10.1146/annurev-polisci-090216-023229 · Cited by 120
“Similarly, using Twitter users from six countries, Barbera (2015) identify Twitter “followers” of three or more political actors and use ideal point estimation methods to recover the ideologies of the politicians and the Twitter users.”
MethodLajevardi, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108782814 · Cited by 110
Fariss, C. J. (2017) · British Journal of Political Science · doi:10.1017/s000712341500054x · Cited by 104
Quantifying Political Leaning from Tweets, Retweets, and Retweeters
Wong, F. M. F., Tan, C. W., Sen, S., & Chiang, M. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2553667 · Cited by 103
“Barber a [5] applied Bayesian ideal point estimation using following actions as observations.”
BackgroundMethod#ISISisNotIslam or #DeportAllMuslims?
Magdy, W., Darwish, K. M., Abokhodair, N., Rahimi, A., & Baldwin, T. (2016) · ACM Web Science Conference (WebSci) · doi:10.1145/2908131.2908150 · Cited by 101
“[Barberá 2015] Pablo Barberá.”
BackgroundHeft, A., Knüpfer, C. B., Reinhardt, S., & Mayerhöffer, E. (2020) · The International Journal of Press/Politics · doi:10.1177/1940161220963670 · Cited by 104
“While studies on partisanship and social media networks have shown how these exhibit strong signs of ideological coherence (Barberá 2015), research on digital counter-publics also highlights that those “cannot be thought without the mainstream public sphere that marginalises [sic] them” (Kaiser and…”
BackgroundRise of the Machines? Examining the Influence of Social Bots on a Political Discussion Network
Hagen, L., Neely, S. R., Keller, T. E., Scharf, R., & Vasquez, F. E. (2020) · Social Science Computer Review · doi:10.1177/0894439320908190 · Cited by 102
“These network structures are similar to those identified in previous analyses of contentious political discussion networks (i.e., Barberá, 2015; Conover et al., 2011; Del Vicario et al., 2017), and they appear to be consistent with the “echo chamber” hypothesis (Sunstein, 2007), suggesting that…”
ResultWhat demographic attributes do our digital footprints reveal? A systematic review
Hinds, J. M., & Joinson, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207112 · Cited by 104
“Barberá [86] directly attempted to address this problem by developing a model that estimated ideology on a continuous scale. By using social ties (i.e. who individuals follow), Barberá [86] successfully inferred ideological alignment (strength in terms of right vs. left leaning) across European countries and the US, t…”
MethodVaccari, C., Valeriani, A., Barberá, P., Jost, J. T., Nagler, J., & Tucker, J. A. (2016) · Social Media + Society · doi:10.1177/2056305116664221 · Cited by 188
“Studies analyzing large quantities of behavioral data likewise suggest that political networks on Twitter exhibit high levels of homophily (Conover et al., 2011; Barberá, 2014; Colleoni, Rozza, & Arvidsson, 2014).”
BackgroundSelf-citationFast Estimation of Ideal Points with Massive Data
Imai, K., Lo, J., & Olmsted, J. (2016) · American Political Science Review · doi:10.1017/s000305541600037x · Cited by 99
Sterling, J. L., Jost, J. T., & Bonneau, R. (2020) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000275 · Cited by 106
Measuring Policy Positions in Political Space
Laver, M. (2014) · Annual Review of Political Science · doi:10.1146/annurev-polisci-061413-041905 · Cited by 94
Gaumont, N., Panahi, M., & Chavalarias, D. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201879 · Cited by 88
The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries
van Vliet, L., Törnberg, P., & Uitermark, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237073 · Cited by 108
“[25, 31, 49]), the TPD allows for studying these topics at scale and in comparative and transnational perspective.”
Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis
Bestvater, S. E., & Monroe, B. L. (2022) · Political Analysis · doi:10.1017/pan.2022.10 · Cited by 93
“For these analyses, we again situate each author in ideological space using Bayesian ideal point estimation based on the information provided by the other accounts they follow, as outlined in Barberá (2015).”
BackgroundMethodInfluentialThe Effect of Social Media on Elections: Evidence from The United States
Fujiwara, T., Müller, K., & Schwarz, C. R. (2023) · Journal of the European Economic Association · doi:10.1093/jeea/jvad058 · Cited by 107
There is no liberal media bias in which news stories political journalists choose to cover
Hassell, H. J. G., Holbein, J. B., & Miles, M. R. (2020) · Science Advances · doi:10.1126/sciadv.aay9344 · Cited by 94
“Barberá ( 25 ) shows that this approach produces ideology measures that are strongly related to individual self-reported measures of ideology and validated party registration records among both the public and elites.”
InfluentialPoliticians unleashed? Political communication on Twitter and in parliament in Western Europe
Silva, B. C., & Proksch, S. (2021) · Political Science Research and Methods · doi:10.1017/psrm.2021.36 · Cited by 98
“…for personal politics ” (Enli and Skogerbø, 2013, p. 771), and studies of Twitter use by members of parliament (MPs) indicate that the follower network (Barberá, 2015) and content posted by MPs (Ecker, 2017) are informative not only about MP ideology, but also of intra-party dynamics such as…”
Computational Social Science and the Study of Political Communication
Theocharis, Y., & Jungherr, A. M. (2020) · Political Communication · doi:10.1080/10584609.2020.1833121 · Cited by 106
“Activity on social media has been used to label users according to their political ideology (Barberá, 2015), psychological traits (Azucar et al., 2018), mental health (Chancellor & Choudhury, 2020), or the authenticity of their account (Rauchfleisch & Kaiser, 2020).”
MethodMedia Influences on Social Outcomes: The Impact of MTV's 16 and Pregnant on Teen Childbearing
Kearney, M. S., & Levine, P. B. (2014) · National Bureau of Economic Research · doi:10.3386/w19795 · Cited by 85
“There are a handful of studies in political science that use Twitter data to identify political ideology and study information and influence networks in this context (cf. Barbera, 2013).”
Segesten, A. D., & Bossetta, M. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1252413 · Cited by 84
Donald Trump: A Critical Theory-Perspective on Authoritarian Capitalism
Fuchs, C. (2017) · tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society · doi:10.31269/triplec.v15i1.835 · Cited by 83
Nationalism on Weibo: Towards a Multifaceted Understanding of Chinese Nationalism
Zhang, Y., Liu, J., & Wen, J.-R. (2018) · The China Quarterly · doi:10.1017/s0305741018000863 · Cited by 84
“See, e.g., Barbera 2015.”
Digital Political Talk and Political Participation: Comparing Established and Third Wave Democracies
Vaccari, C., & Valeriani, A. (2018) · SAGE Open · doi:10.1177/2158244018784986 · Cited by 101
“…alternative, we could have chosen to unobtrusively collect data on users’ behaviors on social media platforms, then analyze these digital trace data with computational techniques to estimate the frequency and contents of political discussion, as well as other characteristics (e.g., Barberá, 2014).”
MethodUnderstanding Popularity, Reputation, and Social Influence in the Twitter Society
García, D., Mavrodiev, P., Casati, D., & Schweitzer, F. (2017) · Policy & Internet · doi:10.1002/poi3.151 · Cited by 82
Predicting the Topical Stance and Political Leaning of Media using Tweets
Stefanov, P., Darwish, K. M., Atanasov, A., & Nakov, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.50 · Cited by 88
“…users, much recent work focused on stance detection to determine a person’s position on a topic including the deduction of political preferences (Barber´a, 2015; Barber and Rivero, 2015; Borge-Holthoefer et al., 2015; Cohen and Ruths, 2013; Colleoni et al., 2014; Conover et al., 2011b; Fowler…”
BackgroundSteppat, D., Castro, L., & Esser, F. (2021) · European Journal of Communication · doi:10.1177/02673231211012141 · Cited by 104
“, 2015), political actors sharing their ideology (Barberá, 2015) or through more homogeneous friend circles (Bakshy et al.”
BackgroundResultInfluentialSterling, J. L., Jost, J. T., & Hardin, C. D. (2019) · SAGE Open · doi:10.1177/2158244019846211 · Cited by 88
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of BipartisanshipPreprint
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01665 · Cited by 101
“We adopt the latent space model proposed by Barberá et al. [7] to estimate a user polarity score.”
BackgroundResultInfluentialCan we vote with our tweet? On the perennial difficulty of election forecasting with social media
Huberty, M. (2015) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.005 · Cited by 73
Coherent campaigns? Campaign broadcast and social messaging
Bode, L., Lassen, D. S., Kim, Y. M., et al. (2016) · Online Information Review · doi:10.1108/oir-11-2015-0348 · Cited by 72
“…examinations of member adoption and frequency of Twitter activity (Evans et al., 2014; Nagler and Tucker, 2015), member/constituent revealed policy preferences on Twitter and their implications for representation (Barbera, 2015), and the effect of member tweets on electoral outcomes (Murthy, 2015).”
BackgroundMarchal, N. (2021) · Communication Research · doi:10.1177/00936502211042516 · Cited by 94
“This is partly explained by the difficulty of inferring the party or ideological affiliations of large numbers of users (Barberá & Alvarez, 2015) and adequately linking sentiment to different types of interactions (Hillmann & Trier, 2012).”
BackgroundInferring Roll‐Call Scores from Campaign Contributions Using Supervised Machine Learning
Bonica, A. (2018) · American Journal of Political Science · doi:10.1111/ajps.12376 · Cited by 72
Echo Chambers, Rabbit Holes, and Algorithmic Bias: How YouTube Recommends Content to Real UsersPreprint
Brown, M. A., Bisbee, J. H., Lai, A., Bonneau, R., Nagler, J., & Tucker, J. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4114905 · Cited by 72
Ideological Asymmetry in the Reach of Pro-Russian Digital Disinformation to United States Audiences
Hjorth, F., & Adler‐Nissen, R. (2019) · Journal of Communication · doi:10.1093/joc/jqz006 · Cited by 68
“Based on these followed accounts, user ideology was then estimated using a variation of the procedure presented in Barberá (2015).”
MethodLearning to See: Convolutional Neural Networks for the Analysis of Social Science Data
Torres, M., & Cantú, F. (2021) · Political Analysis · doi:10.1017/pan.2021.9 · Cited by 71
“…quickly and reliably allows social scientists to analyze information from a large amount of data, such as roll-call votes (McCarty, Poole, and Rosenthal 2006), congressional floor speeches (Dietrich, Enos, and Sen 2019; Dietrich, Hayes, and O’Brien 2019), and social media posts (Barberá 2015).”
BackgroundIssue Yield and Party Strategy in Multiparty Competition
Sio, L. D., Angelis, A. D., & Emanuele, V. (2017) · Comparative Political Studies · doi:10.1177/0010414017730082 · Cited by 67
“As a consequence, Twitter is also increasingly attracting the interest of political behaviour scholars (Barberá, 2015; Dubois & Gaffney, 2014; Vaccari et al., 2013).”
The Media Matters: Muslim American Portrayals and the Effects on Mass Attitudes
Lajevardi, N. (2020) · The Journal of Politics · doi:10.1086/711300 · Cited by 66
“Despite its simplicity, the dictionary approach achieves a high degree of accuracy (see Barberá 2014; González-Bailón and Paltoglou 2015, fig.”
BackgroundMethodSocial Media Campaigning: Mobilization and Fundraising on Facebook
Auter, Z. J., & Fine, J. A. (2017) · Social Science Quarterly · doi:10.1111/ssqu.12391 · Cited by 66
Alizadeh, M., Weber, I. G., Cioffi‐Revilla, C. A., Fortunato, S., & Macy, M. W. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0193-9 · Cited by 72
Informing the Public: How Party Communication Builds Opportunity Structures
Popa, S. A., Fazekas, Z., Braun, D., & Leidecker-Sandmann, M. (2019) · Political Communication · doi:10.1080/10584609.2019.1666942 · Cited by 64
“The chances of stumbling upon specific information posted by politicians is especially high among party supporters as they have been shown to be more likely to follow members of their own party (Barberá, 2015).”
BackgroundPredicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data
Amador, J., Collignon, S., Benoit, K. R., & Matsuo, A. (2017) · Statistics Politics and Policy · doi:10.1515/spp-2017-0006 · Cited by 67
Crilley, R., Gillespie, M., Vidgen, B., & Willis, A. (2020) · The International Journal of Press/Politics · doi:10.1177/1940161220980692 · Cited by 63
“Notably, RT’s audiences are frequently viewed with the same disdain directed at RT itself and are (at least implicitly) considered to be ideologically closely aligned with RT’s political framing on issues and events (Birrell 2018).”
BackgroundDoes negatively toned language use on social media lead to attitude polarization?
Buder, J., Rabl, L., Feiks, M., Badermann, M., & Zurstiege, G. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106663 · Cited by 73
Haroon, M., Wojcieszak, M., Chhabra, A., Liu, X., Mohapatra, P., & Shafiq, Z. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213020120 · Cited by 64
Intra-party politics in 140 characters
Cerón, A. (2016) · Party Politics · doi:10.1177/1354068816654325 · Cited by 62
“…political actors like parties, factions, and individual politicians, this article confirms the promising results obtained by previous studies (e.g. Barberá, 2015; Bond and Messing, 2015; Ecker, 2015) and provides a further contribution by showing that the content of comments published online is…”
MethodResultCarlson, J., & Harris, K. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000860 · Cited by 61
Alshaabi, T., Dewhurst, D. R., Minot, J. R., Arnold, M. V., Adams, J. L., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00271-0 · Cited by 92
Opinion dynamics of online social network users: a micro-level analysis
Kozitsin, I. (2021) · Journal of Mathematical Sociology · doi:10.1080/0022250x.2021.1956917 · Cited by 67
“pinions equal to 0.5. While we recognize that such names of groups are not correct from a political science theory standpoint, the analogies remain pertinent. In Figure 1, we reproduce Figure 2 from (Barberá, 2014b). Namely, we depict how the mean proportion of friends in each of the four groups varies from group to g…”
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
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