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.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“We employ the methodology of Barberá (2015) to measure users' political orientation.”
From Cross-Partisan Interactions on Twitter (2025) · cites this paper as Barberá 2015
The cited source says what the claim attributes to it.
In the citing paper
“Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs (Barberá, 2015;Conover et al., 2011).”
From Credibility of climate change denial in social media (2019) · cites this paper as Barberá, 2015
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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.
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Items 301–400 of 980
Do Islamic State’s Deadly Attacks Disengage, Deter, or Mobilize Supporters?
Barceló, J., & Labzina, E. (2018) · British Journal of Political Science · doi:10.1017/s0007123418000340 · Cited by 15
“Third, our work contributes to the growing literature that employs big data to answer long-standing questions in political science (Barbera 2015; Barbera et al. 2015; King, Pan, and Roberts 2014) by using a novel dataset on IS-related Twitter accounts. Methodologically, our work combines several features of computatio…”
Unfolding the dimensionality structure of social networks in ideological embeddings
Morales, P. R., Cointet, J., & Zolotoochin, G. M. (2021) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3487351.3489441 · Cited by 15
“To filter out inactive or bot accounts, and accounts without enough ideological referential connections, we follow the criteria proposed in [5] and consider only followers that follow at least 3 MPs, and that have at least 25 followers.”
MethodInfluentialIdeological Extremity and Success in Primary Elections
King, A., Orlando, F. J., & Sparks, D. B. (2015) · Social Science Computer Review · doi:10.1177/0894439315595483 · Cited by 19
“In accord with other recent scholarship (Barbera, 2015), we believe Twitter is a valuable data source that allows scholars to expand greatly the universe of individuals whose positions we can map, allowing us to greatly democratize our understanding of ideology in the public sphere.”
BackgroundIdeology Detection for Twitter Users via Link Analysis
Gu, Y., Chen, T., Sun, Y., & Wang, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60240-0_32 · Cited by 17
“4 – SL-IPM : our Single Link Type Ideal Point Estimation Model where only one type of link is present in the social network, as introduced previously.”
BackgroundInfluentialUsing Twitter to Study Public Discourse in the Wake of Judicial Decisions
Clark, T. S., Staton, J. K., Wang, Y., & Agichtein, E. (2018) · Journal of Law and Courts · doi:10.1086/695423 · Cited by 15
Underlying socio-political processes behind the 2016 US election
Bryden, J., & Silverman, E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214854 · Cited by 15
Tella, R. D., Gálvez, R. H., & Schargrodsky, E. (2021) · National Bureau of Economic Research · doi:10.3386/w29458 · Cited by 17
Political Coalitions and Social Media: Evidence from Pakistan
Mir, A., Mitts, T., & Staniland, P. (2022) · Perspectives on Politics · doi:10.1017/s1537592722001931 · Cited by 24
“…the presence of social media “ echo chambers ” (or the lack thereof) and the spread of misinformation, disinforma-tion, and online polarization (Barberá 2015; Barberá et al. 2015; 2019; Coppock, Guess, and Ternovski 2016; Guess et al. 2019; Munger et al. 2022; Tokita et al. 2021; Tucker et…”
BackgroundElection Fraud, YouTube, and Public Perception of the Legitimacy of President Biden
Bisbee, J. H., Brown, M. A., Lai, A., Bonneau, R., Nagler, J., & Tucker, J. A. (2022) · Journal of Online Trust and Safety · doi:10.54501/jots.v1i3.60 · Cited by 15
Inferring attitudinal spaces in social networks
Morales, P. R., Cointet, J., Zolotoochin, G. M., Peralta, A. F., Íñiguez, G., & Pournaki, A. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-01013-4 · Cited by 21
“These methods can be traced back to ideology scaling methods in political sciences (Poole and Rosenthal 1985) in recently in online social networks (Barberá 2015; Bond and Messing 2015).”
BackgroundMethodInfluentialRomenskyy, M., Spaiser, V., Ihle, T., & Lobaskin, V. (2018) · Royal Society Open Science · doi:10.1098/rsos.171935 · Cited by 16
“Studies with social media data have revealed the strong effect of homophily in online social networks, which may lead to phenomena like the echo chamber [34, 35], where opinions are amplified through communication and repetition inside an “enclosed” social system.”
Backgroundde Souza, R. M., da Graça, L. F. G., & Silva, R. S. (2017) · Brazilian Political Science Review · doi:10.1590/1981-3821201700030003 · Cited by 6
News audience fragmentation in the Japanese Twittersphere
Kobayashi, T., Ogawa, Y., Suzuki, T., & Yamamoto, H. (2019) · Asian Journal of Communication · doi:10.1080/01292986.2018.1458326 · Cited by 14
“The most common assumption is that Twitter users selectively follow media and politicians whose ideological positions are similar to their own (e.g. Barberá, 2015).”
BackgroundInfluentialEngagement with news on Twitter: insights from Australia and Korea
Ackland, R., O’Neil, M., & Park, S. (2018) · Asian Journal of Communication · doi:10.1080/01292986.2018.1462393 · Cited by 14
“, 2011) have used tweet content to computationally predict the political affiliation of Twitter users, Barberá, Jost, Nagler, Tucker, and Bonneau (2015) note the challenge of accurately ascer-”
MethodInfluentialCharacterization of Local Attitudes Toward Immigration Using Social Media
Freire-Vidal, Y., & Graells-Garrido, E. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3316455 · Cited by 22
“This behavior is commonly found in networks [5], and it has been documented in Twitter political discussion [10], including in Chile [20].”
BackgroundLiang, F. (2019) · Communication and the Public · doi:10.1177/2057047319894654 · Cited by 14
“These computational methods have been employed to examine various topics in social sciences, including political discourse (Stewart & Zhukov, 2009), comparative politics (Lucas et al., 2015), newspaper coverage (DiMaggio et al., 2013), and social media posts (Barberá, 2014).”
MethodHawks and Doves at the FOMCPreprint
Eijffinger, S. C. W., Mahieu, R. J., & Raes, L. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2570848 · Cited by 14
“The Bayesian ideal points approach developed by Clinton, Jackman, and Rivers (2004) which led to 3 new applications such as the analysis of social network data, see Barberá (2014).”
Tweeting for Peace : Experimental Evidence from the 2016 Colombian Plebiscite
Vásquez, M., Munger, K., Martínez, J. D. S., & Gallego, J. (2022) · Repositorio Institucional E-DocUR (Universidad Del Rosario) · doi:10.48713/10336_14059 · Cited by 14
Active Learning for Probabilistic Record LinkagePreprint
Enamorado, T. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3257638 · Cited by 14
Republicans are flagged more often than Democrats for sharing misinformation on X’s Community Notes
Renault, T., Mosleh, M., & Rand, D. G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502053122 · Cited by 13
“…2023 and June 2024, and infer posters’ partisanship based on their social connections following the methodology proposed by Mosleh and Rand ( 7 ) and Barberá ( 8 ), and resolving classification disagreements between the two using a large language model applied to tweet content; see Methods and SI.”
Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
Luo, R., Nettasinghe, B., & Krishnamurthy, V. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3091168 · Cited by 13
“We could adapt the correspondence analysis or the maximum likelihood logit model in [29] to obtain user’ political ideology at different times and assign political scores accordingly to the user-related tweets.”
MethodTweeting apart: Democratic backsliding, new party cleavage and changing media ownership in Turkey
Metin, O. F., & Morales, P. R. (2022) · Party Politics · doi:10.1177/13540688221135019 · Cited by 29
“…the highest inertia contain information revealing the main party divides in national party politics – see examples in Germany (S¨altzer, 2022), Spain (Theocharis et al., 2015), France (Cointet et al., 2021; Ramaciotti Morales and Cointet, 2021), and the US (Barber´a, 2015; Jachim et al., 2021).”
BackgroundInfluentialWirtschafter, V., & Majumder, S. (2023) · Journal of Online Trust and Safety · doi:10.54501/jots.v2i1.139 · Cited by 18
“…partisan leanings of the 733 unique Twitter users who still had a tweet posted online with a Community Note attached to it on February 24, 2023, we pulled their following networks and estimated their partisan ideologies using a method common to computational social science research (Barberá 2015).”
MethodSharing Followers in e-Government Twitter Accounts: The Case of Greece
Zafiropoulos, K., Antoniadis, K., & Vrana, V. G. (2014) · Future Internet · doi:10.3390/fi6020337 · Cited by 13
“An especially promising aspect is that individuals and politicians interact within the same symbolic framework using messages of identical length, the same type of language, the same external references and, frequently, the same content use of hashtags and retweets [50].”
BackgroundNew media for the new electorate? Congressional outreach to Latinos on Twitter
Gervais, B. T., & Wilson, W. C. (2017) · Politics Groups and Identities · doi:10.1080/21565503.2017.1358186 · Cited by 13
Measuring Congressional Partisanship and Its Consequences
Gelman, J., & Wilson, S. L. (2021) · Legislative Studies Quarterly · doi:10.1111/lsq.12331 · Cited by 13
Align Voting Behavior with Public Statements for Legislator Representation Learning
Mou, X., Wei, Z., Chen, L., Ning, S., He, Y., Jiang, C., & Huang, X. (2021) · doi:10.18653/v1/2021.acl-long.99 · Cited by 13
“Although previous studies (Bruns and Highfield, 2013; Golbeck and Hansen, 2014; Barber´a, 2015; Peng et al., 2016; Wong et al., 2016; Boutyline and Willer, 2017; Johnson et al., 2017) have incorporated social network of following or retweeting on Twitter to learn legislators, fine-grained attitudes…”
BackgroundMethodResultScale, Time, and Activity Patterns: Advanced Methods for the Analysis of Online Networks
Borge‐Holthoefer, J., & Gonzlez-Bailn, S. (2016) · SAGE Publications Ltd eBooks · doi:10.4135/9781473957992.n15 · Cited by 13
Tweeting on Catalonia’s Independence: The Dynamics of Political Discussion and Group Polarisation
Balcells, J., & Padró-Solanet, A. (2016) · Medijske studije · doi:10.20901/ms.7.14.9 · Cited by 13
A Multinomial Framework for Ideal Point Estimation
Goplerud, M. (2018) · Political Analysis · doi:10.1017/pan.2018.31 · Cited by 13
“, Barberá2015); themost classic example involves seeingabinary yi j aseither a “link”or “no link.” A multinomial interpretationmight be for “strong friend,” “friend,” or “not friend.” This relationship can bemodeled simply in the mIRTby again changing the definition ofψ i j . Following Imai, Lo, and Olmsted (2016)’s p…”
BackgroundMethodInfluentialA new dataset of Dutch and Danish party congress speeches
Schumacher, G., Hansen, D., van der Velden, M. A. C. G., et al. (2019) · Research & Politics · doi:10.1177/2053168019838352 · Cited by 13
“…releases (Sagarzazu and Klüver, 2017; Van der Velden et al., 2017); parliamentary speeches (Proksch and Slapin, 2015); media reports about party positions (De Nooy and Kleinnijenhuis, 2013); tweets (Barberá, 2015); bills (Baumgartner et al., 2006); and executive speeches (Jennings et al., 2011).”
BackgroundMeasuring public opinion via digital footprints
Cerina, R., & Duch, R. M. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.10.004 · Cited by 13
“Again, there are recent findings suggesting that Twitter sentiment is correlated with partisan voter registration data [17] and that Facebook likes predict reported vote choice [18].”
BackgroundResultAlvarez, R. M., Adams-Cohen, N., Kim, S. S., & Li, Y. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108887359 · Cited by 13
Social Media vs. Democracy: Evidence from the January 6th Insurrection
Müller, K., Schwarz, C. R., & Shen, Z. (2026) · CESifo · doi:10.65864/qlfi14z2yh · Cited by 14
Political Ideology and Polarization: A Multi-dimensional Approach
Sinno, B., Oviedo, B., Atwell, K., Alikhani, M., & Li, J. J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.17 · Cited by 12
“, 2016) and social media text at the electorate level (SaezTrumper et al., 2013; Barberá, 2015).”
BackgroundCross-Platform Partisan Positioning in Congressional Speech
Green, J., Shoub, K., Blum, R., & Cormack, L. (2024) · Political Research Quarterly · doi:10.1177/10659129241236685 · Cited by 14
“…continuous measures of estimated latent spaces such as DW-NOMINATE legislator “ ideology ” (e.g., Bonica 2014; Grif fi n 2008; Lindgren and Southwell 2014; Barber´a 2015), though elsewhere scholars argue that this dimension is better understood as re fl ecting partisanship (Lee 2009, 2016; Noel…”
InfluentialWang, Y., Luo, J., & Zhang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67217-5_25 · Cited by 12
“Social clustering, on the other hand, is analyzed in (McPherson, Smith-Lovin, and Cook 2001; Barberá 2015).”
MethodAbi-Hassan, S., Box‐Steffensmeier, J. M., Christenson, D. P., et al. (2023) · Political Analysis · doi:10.1017/pan.2022.34 · Cited by 12
“The coloring suggests that much like other political networks (e.g., Adamic and Glance 2005; Barberá 2015), what we see before the Court is a polarized network of two ideological communities, the conservative and the liberal.”
BackgroundInfluentialGarimella, V. R. K., & Weber, I. G. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631820 · Cited by 12
“Several papers have looked at various Twitter user classification tasks, typically for (i) political orientation in the US, (ii) gender, and (iii) age [19, 21, 26, 7, 8, 3].”
BackgroundDiverging Projections of Reality
Knüpfer, C. B. (2017) · Journalism Studies · doi:10.1080/1461670x.2017.1387072 · Cited by 12
“In other cases, the term has been used to assess the conditions under which social media might come to foster (political) homophily (Barberá 2015).”
BackgroundZubiaga, A., Wang, B., Liakata, M., et al. (2019) · IEEE Intelligent Systems · doi:10.1109/mis.2019.2958393 · Cited by 12
Emerging trends: Unfair, biased, addictive, dangerous, deadly, and insanely profitable
Church, K., Schoene, A. M., Ortega, J. E., Chandrasekar, R., & Kordoni, V. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000481 · Cited by 14
“3. polarization/homophily ap,aq (McPherson, Smith-Lovin, and Cook 2001; De Koster and Houtman 2008; Colleoni, Rozza, and Arvidsson 2014; Bakshy, Messing, and Adamic 2015; Barberá 2015; Kurka, Godoy, and Zuben 2016; Allcott and Gentzkow 2017; Fourney et al .”
Generalized Ideal Point Models for Robust Measurement with Dirty Data in the Social SciencesPreprint
Kubinec, R. (2019) · doi:10.31219/osf.io/8j2bt · Cited by 12
Age, Generation, and Political Participation
Serra, L., & Smets, K. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198861126.013.32 · Cited by 11
Anti-vaccine rabbit hole leads to political representation: the case of Twitter in Japan
Toriumi, F., Sakaki, T., Kobayashi, T., & Yoshida, M. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-023-00241-8 · Cited by 11
“[35]).”
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
Zhang, C., Zhou, Z., Peng, X., et al. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31424 · Cited by 11
Unveiling the hidden agenda: Biases in news reporting and consumption
Galeazzi, A., Peruzzi, A., Brugnoli, E., Delmastro, M., & Zollo, F. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae474 · Cited by 14
Learning from noisy label proportions for classifying online social data
Ardehaly, E. M., & Culotta, A. (2017) · Social Network Analysis and Mining · doi:10.1007/s13278-017-0478-6 · Cited by 12
“2013a), political affiliation (Conover et al. 2011; Barberá 2013; Volkova and Van Durme 2015), and occupation (Preotiuc-Pietro et al.”
BackgroundIbenskas, R., & Bunea, A. (2020) · European Journal of Political Research · doi:10.1111/1475-6765.12412 · Cited by 11
“We recognize this social media platform as a rich and relevant source of information about legislators’ communication and ties to various publics (Barberá 2015) and build on the research emphasizing the importance of Twitter as a tool carefully used by MEPs as part of their political communication strategies (Fazekas…”
Aruguete, N. (2021) · El Profesional de la Informacion · doi:10.3145/epi.2021.mar.18 · Cited by 11
“Quienes encuentran evidencia contraria a la existencia de “burbujas” reportan que las redes sociales no necesariamente producen un cambio de actitud y que, por ende, no va de suyo que los individuos sólo busquen amigos que piensen como ellos (Bakshy; Messing; Adamic, 2015; Barberá, 2015).”
BackgroundInfluentialDecomposing attitude distributions to characterize attitude polarization in Europe
Gestefeld, M., Lorenz, J., Henschel, N. T., & Boehnke, K. (2022) · SN Social Sciences · doi:10.1007/s43545-022-00342-7 · Cited by 14
“A topic of some debate is the contribution of social media to polarization (Barberá 2014, 2015; Garimella and Weber 2017; Eady et al.”
BackgroundWalk, E., Garimella, K., & Christia, F. (2023) · World Development · doi:10.1016/j.worlddev.2023.106268 · Cited by 11
Ideological asymmetries in online hostility, intimidation, obscenity, and prejudice
Badaan, V., Hoffarth, M. R., Roper, C., Parker, T., & Jost, J. T. (2023) · Scientific Reports · doi:10.1038/s41598-023-46574-2 · Cited by 15
“We estimated user ideology scores based on Barberá’s method 36 and observed that whereas liberal users were slightly more likely than their conservative counterparts to use obscene language, conservatives were more likely to use negative prejudice, name-calling, and hateful and threatening…”
MethodUsing Crowd-Sourced Data to Study Public Services: Lessons from Urban India
Post, A. E., Agnihotri, A., & Hyun, C. (2018) · Studies in Comparative International Development · doi:10.1007/s12116-018-9271-4 · Cited by 11
“Some efforts focus on the measurement of political attitudes or ideology (Barberá, 2015; Boutet, Antoine, Hyoungshick Kim, and Eiko Yoneki, 2012; Calvo, 2015; Jamal, Keohane, Romney, & Tingley, 2015), while others have used crowd-sourced data to map political protests and understand their dynamics…”
BackgroundHostilité et prosélytisme des communautés politiques
Chavalarias, D., Gaumont, N., & Panahi, M. (2019) · Réseaux · doi:10.3917/res.214.0067 · Cited by 11
Shu, F., & Howell, W. G. (2020) · Presidential Studies Quarterly · doi:10.1111/psq.12645 · Cited by 11
Deep Learning for Political Science
Chatsiou, K., & Mikhaylov, S. (2020) · SAGE Publications Ltd eBooks · doi:10.4135/9781526486387.n58 · Cited by 19
“estimate party positions from texts and the increasing availability of annotated political corpora, the availability and complexity of quantitative text-analysis methods have increased dramatically (Barberá, 2015; Grimmer and Stewart, 2013; Herzog and Benoit, 2015; Lauderdale and Herzog, 2016). Most of these methods t…”
BackgroundUsing Computational Text Analysis Tools to Study African Online News Content
Madrid‐Morales, D. (2020) · African Journalism Studies · doi:10.1080/23743670.2020.1820885 · Cited by 11
Some conditions (not) affecting selection neglect: Evidence from the lab
López‐Pérez, R., Pintér, Á., & Sánchez-Mangas, R. (2022) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2021.12.036 · Cited by 11
“4: Twitter users might tend to participate in political conversations that they find consistent with their ideology (e.g., Barberá, 2014).”
BackgroundMorales, P. R., Cointet, J., & Laborde, J. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381468 · Cited by 11
“Examples include the UK, Spain, Italy, and Netherlands [12]).”
BackgroundInfluentialUnderstanding Online Political Networks: The Case of the Far-Right and Far-Left in Greece
Agathangelou, P., Katakis, I., Rori, L., Gunopulos, D., & Richards, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67217-5_11 · Cited by 11
Lyu, Z. (2023) · New Media & Society · doi:10.1177/14614448231180654 · Cited by 9
“The latent space model assumes that Twitter users are pre-disposed to follow politicians with whom they share ideological similarities and avoid those with whom they disagree ideologically (Barberá, 2015).”
InfluentialTang, J. L., Yan, B., Chang, H. H.-C., et al. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107922 · Cited by 10
Rasmussen, S. H. R., & Petersen, M. B. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad382 · Cited by 18
“The most popular current measure toward measuring network ideology is based on Pablo Baberás novel approach (64), which estimates an individual’s ideology based on whether they follow or befriend known political actors with clear ideologies.”
Jost, P., Heft, A., Buehling, K., Zehring, M., Schulze, H., Bitzmann, H., & Domahidi, E. (2023) · Medien & Kommunikationswissenschaft · doi:10.5771/1615-634x-2023-3-4-212 · Cited by 11
Iranzo-Cabrera, M., Castro-Bleda, M. J., Simón-Astudillo, I., & Hurtado, L.-F. (2024) · Social Science Computer Review · doi:10.1177/08944393241269417 · Cited by 22
“It is dif fi cult to perceive because Twitter users tend to follow homophilic political actors (Barber´a, 2015) and people within their social characteristics (Zhang & Ho, 2022).”
Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study
Rao, A., Sabri, N., Guo, S., Raschid, L., & Lerman, K. (2024) · Journal of Medical Internet Research · doi:10.2196/63910 · Cited by 13
“The ideology estimates were obtained from the study by McCabe [98] and were calculated using methods described in the study by Barberá [99].”
Bonica, A. (2016) · RSF The Russell Sage Foundation Journal of the Social Sciences · doi:10.7758/rsf.2016.2.7.02 · Cited by 10
“…contributions (McCarty and Poole 1998), political text (Laver, Benoit, and Garry 2003; Monroe and Maeda 2004; Monroe, Colaresi, and Quinn 2008; Slapin and Proksch 2008), co- sponsorship networks (Peress 2013), voter evaluations (Hare et al. 2014), and Twitter follower networks (Barberá 2015).”
Innovations in concepts and measurement for the study of peace and conflict
Fariss, C. J., & Lo, J. (2020) · Journal of Peace Research · doi:10.1177/0022343320975200 · Cited by 10
“…Epstein, 2005; Poole, 2005; Poole & Rosenthal, 1991, 1997; Treier & Hillygus, 2009), sometimes in other democratic contexts (Alema´n & Saiegh, 2007; Barbera´, 2015; Desposato, 2006; Lo, Proksch & Gschwend, 2014; Lo, Proksch & Slapin, 2016; Rosenthal & Voe-ten, 2004) and a few examples in…”
Burghartswieser, D., & Rothmund, T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259445 · Cited by 13
“number of studies, including our own [7, 8, 34, 68].”
Who gets a say in this? Speaking security on social media
Umansky, N. (2022) · New Media & Society · doi:10.1177/14614448221111009 · Cited by 17
“The micro-blogging site Twitter, for example, has become one of the main communication channels in daily politics (Barberá, 2015).”
BackgroundInfluentialAŞKUN, V. (2023) · Erciyes Akademi · doi:10.48070/erciyesakademi.1281544 · Cited by 13
“Örneğin, sosyal bilimciler sosyal medya verilerini incelemek için yapay zekâ algoritmalarını kullanarak kamuoyu duyarlılığı, siyasi kutuplaşma ve bilgi yayılımına ilişkin içgörüleri ortaya çıkarmıştır (Barberá, 2015; Tufekci, 2014).”
Political Speech from Corporate America: Sparse, Mostly for Democrats, and Somewhat Representative
Barari, S. (2024) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2024.icwsm.5 · Cited by 9
Baviera, T., Sampietro, A., & García-Ull, F. J. (2019) · The Communication Review · doi:10.1080/10714421.2019.1599642 · Cited by 12
“In fact, homophily on social networks allowed Barberá (2015) to infer Twitter users’ ideological positions by analyzing the political actors they follow.”
BackgroundCoronavirus en Argentina. Polarización partidaria, encuadres mediáticos y temor al riesgo
Aruguete, N., & Calvo, E. F. (2020) · REVISTA SAAP · doi:10.46468/rsaap.14.2.a2 · Cited by 10
“…(Iyengar & Hann, 2009), y se expresan en una variedad de planos de nuestros intercambios cotidianos, tales como la discusión política en el lugar de trabajo, las charlas familiares y, con mayor potencia en el último tiempo, en nuestra exposición e interacción en las redes sociales (Barberá, 2015).”
BackgroundWhat Sounds “Right” to Me? Experiential Factors in the Perception of Political Ideology
Shen, Q., & Rosé, C. P. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.152 · Cited by 10
“Some examples of this include using author interactions with politicians with known party affiliations (Djemili et al., 2014; Barberá, 2015), ideological communities (Chandrasekharan et al.”
BackgroundHow much can you say in a tweet? An approach to political argumentation on Twitter
Elliott‐Maksymowicz, K., Nikolaev, A. G., & Porpora, D. V. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00794-x · Cited by 10
The Impact of Data Persistence Bias on Social Media Studies
Elmas, T. (2023) · ACM Web Science Conference (WebSci) · doi:10.1145/3578503.3583630 · Cited by 14
“We reproduce the study by Barbera [3].”
pvsR: An Open Source Interface to Big Data on the American Political Sphere
Matter, U., & Stutzer, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130501 · Cited by 12
“As a consequence, entries in political blogs and tweets leave electronic traces which can be used to generate, for example, a new kind of data on political opinion formation and behavior [19], on political polarization [20], as well as on political discourse [21] (note that the author of [20] also contributes an OSI-l…”
MethodWihbey, J., Coleman, T. D., Joseph, K., & Lazer, D. M. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.06727 · Cited by 10
“Our method differs from [3] in that we are only interested in inferring ideology of a set of users (journalists).”
BackgroundMethodInfluentialPolarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish TwitterspherePreprint
Chen, T. H. Y., Salloum, A., Gronow, A., Ylä‐Anttila, T., & Kivelä, M. (2020) · doi:10.31235/osf.io/49qgu · Cited by 10
“This logic of aggregating relational behaviour on social media to infer positions has been applied elsewhere to measure political ideology (Barberá, 2015; Conover et al., 2011), and has a number of advantages.”
MethodImmigration, Political Ideologies and the Polarization of American PoliticsPreprint
Dreher, A., Langlotz, S., Matzat, J., & Parsons, C. R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3754680 · Cited by 11
“14A number of recent papers use these data (e.g., Bonica 2013, Thomsen 2014, Barberá 2015, Nyhan and Montgomery 2015, Barber 2016, de Benedictis-Kessner and Warshaw 2016, Hollibaugh Jr and Rothenberg 2018, Martin and Peskowitz 2018, Autor et al. 2020).”
Measuring receptivity to misinformation at scale on a social media platform
Tokita, C. K., Aslett, K., Godel, W., Sanderson, Z., Tucker, J. A., Nagler, J., Persily, N., & Bonneau, R. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae396 · Cited by 14
“In this paper, we use ideology as the predictor of article belief because it is one of the best predictors of belief in misinformation (13, 21 – 24) and can be reliably estimated from social media data (42).”
BackgroundMethodInfluentialBohr, J. (2020) · Environmental Politics · doi:10.1080/09644016.2020.1778990 · Cited by 9
“Barberá (2015) estimated the ideological positions of politicians via the structure of their Twitter follower network, which replicated conventional estimations of ideological position such as DWNOMINATE scores (Poole and Rosen 2007).”
BackgroundPredictive modeling to study lifestyle politics with Facebook likes
Praet, S., Aelst, P. V., van Erkel, P. F. A., der Veeken, S. V., & Martens, D. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00305-7 · Cited by 9
Robinson, M. A. (2022) · doi:10.1093/oso/9780197611555.001.0001 · Cited by 9
Weschle, S. (2017) · British Journal of Political Science · doi:10.1017/s0007123417000023 · Cited by 8
Borucki, I. R. (2017) · Springer eBooks · doi:10.1007/978-3-658-20589-8_8 · Cited by 9
Scaling Court Decisions with Citation Networks
Arnold, C., Engst, B. G., & Gschwend, T. (2021) · Journal of Law and Courts · doi:10.1086/717420 · Cited by 9
“Following Barberá (2015), we solve local identification of our model with a standard normal distribution for θ i and with a normal distribution with a mean at 0 and standard deviation σ α for α j .”
MethodMeasuring the Accuracy of Social Network Ideological Embeddings Using Language Models
Morales, P. R., & Zolotoochin, G. M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96293-7_24 · Cited by 9
Cowburn, M., & Sältzer, M. (2024) · Political Science Research and Methods · doi:10.1017/psrm.2023.62 · Cited by 11
Casarin, R., Correa, J. C., Camargo, J. E., et al. (2019) · Journal of Information Science · doi:10.1177/0165551519886056 · Cited by 9
“Some of the most recent and substantial developments in political analysis with Twitter data show its use as an outlet for propagating political ideologies [15], exerting influence [16], generating political polarisation [17] or classifying ideology of both politicians and voters depending on which Twitter users they…”
BackgroundResultIdeology Detection for Twitter Users with Heterogeneous Types of LinksPreprint
Gu, Y., Chen, T., Sun, Y., & Wang, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08207 · Cited by 17
“• The Bayesian Ideal Point Estimation Model (B-IPM) [2] to be introduced in Section 5.”
Predicting the Topical Stance of Media and Popular Twitter UsersPreprint
Stefanov, P., Darwish, K. M., Atanasov, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01260 · Cited by 9
Marchal, N. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3671497 · Cited by 10
Network-Activated Frames (NAF), Redefining Framing in a New Digital Era
Aruguete, N. (2019) · Springer eBooks · doi:10.1007/978-981-13-2262-4_55-1 · Cited by 9
Measurement Considerations for Quantitative Social Science Research Using Social Media DataPreprint
Ladd, J. M., Ryan, R. M., Singh, L., Bode, L., Budak, C., Conrad, F. G., Cooksey, E. C., Davis-Kean, P. E., Dworak-Fisher, K., Freelon, D., et al. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/ga6nc · Cited by 10
Arranz, A. M., Askland, H. H., Box, Y., et al. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2022.102591 · Cited by 8
Categorizing the non-categorical: the challenges of studying gendered phenomena online
Shugars, S., Quintana, A., Lange, R., & Lazer, D. M. J. (2023) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmad053 · Cited by 8
Inference of social media opinion trends in 2022 Italian elections
Zollo, S., Cinelli, M., Etta, G., Cerqueti, R., & Quattrociocchi, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126377 · Cited by 8
Metadata
- Authors
- Barberá, P.
- Affiliations
- New York University (US)
- Corresponding author
- Barberá, Pablo
- ORCID
- Barberá, Pablo
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2015
- Volume
- 23
- Issue
- 1
- Pages
- 76-91
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2161834943
- Semantic Scholar
- 119306396df0ec0c751aca034c11a36c45338dde
- Cited by
- 982
- References
- 86
- Citations by year
- 2026: 46 · 2025: 89 · 2024: 80 · 2023: 100 · 2022: 121 · 2021: 87 · 2020: 133 · 2019: 81 · 2018: 74 · 2017: 82 · 2016: 48 · 2015: 26
- FWCI
- 165.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Political Analysis (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1248077)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- Twitter, ideal points, political debate, legislators, political parties, party registration, 2012 presidential election, online political behavior, latent variable models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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