The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index; none of the 10 sources its claims cite is checked for support yet.
Political Polarization on Twitter
Open accessAbstract
In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Conover et al. analyze political communication on Twitter during the six weeks before the 2010 U.S. midterm elections, using retweet and mention networks built from 252,300 politically relevant tweets. They find that the retweet network is highly segregated along partisan lines, while the mention network is dominated by a single politically heterogeneous cluster, with cross-ideological interaction linked to hashtag content injection.
Key findings
- The retweet network is highly polarized: its modularity is 0.48 versus 0.17 for the mention network, and its modularity z-score against degree-preserving shuffled graphs is 11.02 versus 2.06 for mentions. In the retweet network, cluster A is 93.4% right-leaning and cluster B is 80.1% left-leaning.
- The mention network is politically heterogeneous: it is dominated by a single cluster accounting for more than 97% of users, and cross-ideological links are much more common than in retweets. Observed/expected link ratios for cross-ideological interaction are 0.68 (Left→Right) and 0.77 (Right→Left) in mentions, versus 0.05 and 0.03 in retweets.
- Manual annotation found most politically active users express a political identity: both annotators could not determine identity for only 8% of users, and more than 75% had unambiguous political valences when requiring both annotators to agree. Cohen’s Kappa was 0.80 for left and 0.82 for right, but only 0.42 for undecidable.
- Hashtag content injection helps explain the difference: users with neutral or mixed-valence hashtags receive and produce more inter-ideological mentions, and nearly 30% of tweets in the #p2 search feed originated from right-leaning users. Example valences include #tcot = 0.384 and #p2 = -0.605.
Methods
- Data were collected from Twitter’s gardenhose API between September 14 and November 1, 2010, observing approximately 355 million tweets. Political content was identified using seed hashtags #p2 and #tcot, a Jaccard similarity threshold of 0.005, 66 unique hashtags, and exclusion of 11 ambiguous hashtags, yielding 252,300 politically relevant tweets from more than 45,000 users.
- The authors constructed retweet and mention networks. The retweet network had 23,766 non-isolated nodes out of 45,365 total, with an 18,470-node largest connected component; the mention network had 10,142 non-isolated nodes out of 17,752 total, with a 7,175-node largest component. Analyses focused on the largest connected components.
- Community detection used label propagation seeded by leading-eigenvector modularity maximization for two communities, run 100 times and compared with Adjusted Rand Index. The authors compared observed modularity to 1,000 degree-preserving shuffled graphs, manually annotated 1,000 random users with a non-author judge on 200, and analyzed observed versus expected cross-ideological links and hashtag valence.
Limitations
- The paper states that direct modularity comparison is problematic because the retweet and mention networks differ in size and connectivity, so it uses shuffled graphs as a baseline; it also notes that the normality assumption for modularity values is false and uses Chebyshev’s inequality instead. It further states that cross-ideological interactions are “almost certainly not a panacea” and may reinforce polarization, and calls for further sentiment analysis and follower-network studies.
- Evident from the text: the sample is limited to six weeks before the 2010 U.S. midterm elections and to tweets containing a discovered set of political hashtags, so findings may not generalize to other periods, elections, or political contexts.
- Evident from the text: manual political alignment was based on 1,000 users, the undecidable category had only fair-to-moderate inter-annotator agreement (κ = 0.42), and the mention network is smaller than the retweet network, which may affect comparisons.
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Claims the paper makes
30 Own result · 13 Own interpretation · 5 No full text of the sources · 4 Own method · 3 Sources not found · 3 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 2 shows that in the mention network, users placed in the same cluster are not likely to be much more similar to each other than users in different clusters.”
§ Political Polarization on Twitter > 3.2 Content Homogeneity
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“We conclude that the community structure found in the retweet network is significantly more segregated than that found in the mention network.”
§ Political Polarization on Twitter > 3.1 Community Structure
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, we note that in both mention and retweet networks, one of the clusters is more cohesive than the other -meaning the tag usage within one community is more homogeneous.”
§ Political Polarization on Twitter > 3.2 Content Homogeneity
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All differences are significant at the 95% confidence level.”
§ Political Polarization on Twitter > 3.2 Content Homogeneity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Based on this analysis it is clear that a majority of politically active users on Twitter express a political identity in their tweets.”
§ Political Polarization on Twitter > 3.3 Political Polarization
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result the average similarity within retweet clusters is higher than across clusters.”
§ Political Polarization on Twitter > 3.2 Content Homogeneity
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instead we find that it is dominated by a politically heterogeneous cluster accounting for more than 97% of the users”
§ Political Polarization on Twitter > 3.3 Political Polarization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“messages in this style make up a substantial portion of the results.”
§ Political Polarization on Twitter > 4.2 Content Injection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A key finding of this study, these results indicate that users contributing to a politically balanced combination of content streams on average receive and produce more inter-ideological communication than those who use mostly partisan hashtags.”
- Also stated in: § 5 Conclusions · hedged
“Specifically, users who apply hashtags with neutral or mixed valence are more likely to engage in communication with opposing communities.”
§ Political Polarization on Twitter > 4.3 Political Valence
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Any Twitter user can select arbitrary hashtags to annotate his or her tweets.
- Also stated in: § 5 Conclusions · hedged
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 content is frequently disparaging of the identities and views associated with users across the partisan divide.”
§ Political Polarization on Twitter > 5 Conclusions
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 10
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
“We observe that the retweet and mention networks exhibit very similar scale-free topology (power-law degree distribution not shown), with a number of users receiving or spreading a huge amount of information.”
§ Political Polarization on Twitter > 2.3 Political Communication Networks
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left-and right-leaning users.”
- Also stated in: § 1 Introduction · boosted
“We demonstrate that the retweet network exhibits a highly modular structure, segregating users into two homogenous communities corresponding to the political left and right.”
- Also stated in: § 3.1 Community Structure · plain
“In summary, the retweet network contains two clusters of users who preferentially propagate content within their own communities.”
§ Political Polarization on Twitter > Abstract
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 1 Introduction · boosted
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
“On the other hand, in the retweet network, users in cluster A are more likely to have very similar profiles than users in cluster B, and users in different clusters are the least similar to each other.”
§ Political Polarization on Twitter > 3.2 Content Homogeneity
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The retweet network exhibits two distinct communities of users, while the mention network is dominated by a single massive cluster of interconnected users.”
- Also stated in: § 3.1 Community Structure · plain
“Figure 1 shows the retweet and mention networks, laid out using a force-directed layout algorithm (Fruchterman and Reingold 1991), with node colors determined by the assigned communities.”
§ Political Polarization on Twitter > 3.1 Community Structure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 3.1 Community Structure · 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
“Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets.”
- Also stated in: § 1 Introduction · plain
“In contrast, we find that the mention network does not exhibit this kind of political segregation, resulting in users being exposed to individuals and information they would not have been likely to choose in advance.”
- Also stated in: § 1 Introduction · plain
“These data also show the mention network to be politically heterogeneous, with users of opposing political views interacting at a much higher rate than in the retweet network”
§ Political Polarization on Twitter > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
“Manual classification of Twitter users by political alignment, demonstrating that the retweet network clusters correspond to the political left and right.”
- Also stated in: § 3 Cluster Analysis · boosted
“by manually annotating users, we show that the retweet network is polarized on a partisan basis, while the mention network is much more politically heterogeneous.”
§ Political Polarization on Twitter > 1 Introduction
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 3 Cluster Analysis · boosted
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 modularity induced by the communities in the retweet and mention networks have values of 0.48 and 0.17, respectively.”
§ Political Polarization on Twitter > 3.1 Community Structure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The 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
“nearly 30% of the tweets in the #p2 search feed would originate from right-leaning users.”
§ Political Polarization on Twitter > 4.2 Content Injection
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Increasing the number of target communities in the mention network does not reveal a more fine-grained ideological structure, but instead results in smaller yet politically heterogeneous clusters.”
§ Political Polarization on Twitter > 3.3 Political Polarization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While we know for certain that ideologically-opposed users interact with one another, either through mentions or content injection, they very rarely share information from across the divide with other members of their community.”
§ Political Polarization on Twitter > 5 Conclusions
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These two means of communication -retweets and mentions-serve distinct and complementary purposes, together acting as the primary mechanisms for explicit, public user-user interaction on Twitter.”
§ Political Polarization on Twitter > 2.1 The Twitter Platform
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Mentions function differently, allowing someone to address a specific user directly through the public feed, or, to a lesser extent, refer to an individual in the third person (Honeycutt and Herring 2008).”
§ Political Polarization on Twitter > 2.1 The Twitter Platform
What the sources say
- Not matchedcites Honeycutt and Herring 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- 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
“In line with the standards of the field, we had a non-author judge with a broad knowledge of politics annotate 200 random users from the set of 1,000 to establish the reproducibility of this annotation scheme.”
§ Political Polarization on Twitter > 3.3 Political Polarization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“For agreement between the 'left' and 'right' categories we report κ = 0.80 and κ = 0.82 respectively, both of which fall in the "nearly perfect agreement" range (Landis and Koch 1977).”
§ Political Polarization on Twitter > 3.3 Political Polarization
What the sources say
- Not checked — no full text of the sourcecites Landis and Koch 1977 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Retweets act as a form of endorsement, allowing individuals to rebroadcast content generated by other users, thereby raising the content's visibility (boyd, Golder, and Lotan 2008).”
§ Political Polarization on Twitter > 2.1 The Twitter Platform
What the sources say
- Not matchedcites boyd, Golder, and Lotan 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- 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
“Such a high agreement suggests that the clusters are consistent, and therefore we avoid resorting to consensus clustering for simplicity.”
§ Political Polarization on Twitter > 3.1 Community Structure
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“We argue that this process results in users being exposed to content they are not likely to rebroadcast, but to which they may respond using mentions, and provide statistical evidence in support of this hypothesis.”
§ Political Polarization on Twitter > 1 Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Each hashtag identifies a stream of content, with users' tag choices denoting participation in different information channels.
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
“Adamic and Glance (2005) famously demonstrated that political blogs preferentially link to other blogs of the same political ideology, a finding supported by the work of Hargittai, Gallo, and Kane (2007).”
§ Political Polarization on Twitter > 1 Introduction
What the sources say
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Consumers of online political information tend to behave similarly, choosing to read blogs that share their political beliefs, with 26% more users doing so in 2008than 2004(Pew Internet and American Life Project 2008).”
§ Political Polarization on Twitter > 1 Introduction
What the sources say
- Not matchedcites Pew Internet and American Life Project 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
“Despite this, some empirical evidence suggests that politically active web users tend to organize into insular, homogenous communities segregated along partisan lines.”
§ Political Polarization on Twitter > 1 Introduction
What the sources say
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“For the undecidable category we found "fair to moderate" agreement (κ = 0.42), indicating that there are users for whom a political identity might be discernible in the context of specific domain knowledge.”
§ Political Polarization on Twitter > 3.3 Political Polarization
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“Many messages contain sentiments more extreme than you would expect to encounter in face-to-face interactions”
§ Political Polarization on Twitter > 5 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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
“We propose that when a user is exposed to ideologically opposed content in this way, she will be unlikely to rebroadcast it, but may choose to respond directly to the originator in the form of a mention.”
§ Political Polarization on Twitter > 4.2 Content Injection
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Any Twitter user can select arbitrary hashtags to annotate his or her tweets.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and ostensibly serves as a place where progressives can once again be exposed only to content aligned with their views.”
§ Political Polarization on Twitter > 4.2 Content Injection
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: Any Twitter user can select arbitrary hashtags to annotate his or her tweets.
The paper relies on this without support.
When politically active individuals can avoid people and information they would not have chosen in advance, their opinions are likely to become increasingly extreme as a result of being exposed to more homogeneous viewpoints and fewer credible opposing opinions.
“The concern is that when politically active individuals can avoid people and information they would not have chosen in advance, their opinions are likely to become increasingly extreme as a result of being exposed to more homogeneous viewpoints and fewer credible opposing opinions.”
The paper relies on this without support.
A deliberative democracy relies on a broadly informed public and a healthy ecosystem of competing ideas.
“A deliberative democracy relies on a broadly informed public and a healthy ecosystem of competing ideas.”
The paper relies on this without support.
Each hashtag identifies a stream of content, with users' tag choices denoting participation in different information channels.
“Each hashtag identifies a stream of content, with users' tag choices denoting participation in different information channels.”
The authors state this as an assumption.
The modularities of shuffled graph cluster assignments are normally distributed.
“This discussion assumes that the modularities of the shuffled graph cluster assignments are distributed normally, which is not true in general.”
The paper relies on this without support.
The partisan nature of U.S. political discourse makes two a natural number of clusters.
“While the partisan nature of U.S. political discourse makes two a natural number of clusters”
The paper relies on this without support.
Any Twitter user can select arbitrary hashtags to annotate his or her tweets.
“Any Twitter user can select arbitrary hashtags to annotate his or her tweets.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Political Polarization on Twitter > 2.1 The Twitter Platform
“between September 14th and November 1st, 2010”
§ Political Polarization on Twitter > 2.1 The Twitter Platform
“During the six weeks of data collection”
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · cited 2×
“For agreement between the 'left' and 'right' categories we report κ = 0.80 and κ = 0.82 respectively, both of which fall in the " nearly perfect agreement " range (Landis and Koch 1977).”
BackgroundInfluentialContent Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,346
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · cited 2×
“Modularity (Newman and Girvan 2004) resulting from the cluster assignments offers a first measure of segregation , and reinforces the qualitative finding above.”
ResultHubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Graph drawing by force‐directed placement
Fruchterman, T. M. J., & Reingold, E. M. (1991) · Software: Practice and Experience · doi:10.1002/spe.4380211102 · Cited by 6,352 · cited 1×
“Figure 1 shows the retweet and mention networks, laid out using a force-directed layout algorithm (Fruchterman and Reingold 1991), with node colors determined by the assigned communities.”
MethodFinding community structure in networks using the eigenvectors of matrices
Newman, M. E. J. (2006) · Physical Review E · doi:10.1103/physreve.74.036104 · Cited by 4,799 · cited 2×
“To improve its effectiveness and stability , we seeded the algorithm with initial node labels determined by the leading-eigenvector modularity maximization method for two clusters (Newman 2006).”
MethodNear linear time algorithm to detect community structures in large-scale networks
Raghavan, U. N., Albert, R., & Kumara, S. R. T. (2007) · Physical Review E · doi:10.1103/physreve.76.036106 · Cited by 3,535 · cited 1×
“Label propagation (Raghavan, Albert, and Kumara 2007) works by assigning an initial arbitrary cluster membership to each node and then iteratively updating each node's label according to the label that is shared by most of its neighbors.”
BackgroundPredicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647 · cited 1×
“Social media play an important role in shaping political discourse in the U.S. and around the world (Bennett 2003; Benkler 2006; Sunstein 2007; Farrell and Drezner 2008; Aday et al. 2010; Tumasjan et al. 2010; O'Connor et al. 2010).”
BackgroundThe political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
Boyd, D., Golder, S., & Lotan, G. (2010) · 2010 43rd Hawaii International Conference on System Sciences · doi:10.1109/hicss.2010.412 · Cited by 2,051 · cited 1×
“Retweets act as a form of endorsement, allowing individuals to rebroadcast content generated by other users, thereby raising the content's visibility (boyd, Golder, and Lotan 2008).”
Background
Research works printed but not linked: 1
- Sunstein, C. R. 2007. Republic.com 2.0. Princeton Univer- sity Press.
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Siyasal İletişim Faaliyetlerinde Söylem: X Üzerine Bir Araştırma
KARACA, M. F., Kazancı, P., & Uzun, G. (2024) · Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.52642/susbed.1468939 · Cited by 2
The dynamics of role evolution in online learning communities
Xing, W., Pei, B., Zhu, W., Li, H., & Guo, R. (2024) · Distance Education · doi:10.1080/01587919.2024.2401085 · Cited by 2
“Other studies highlight the importance of shared values or perspectives, such as political alignment, within social networks (Conover et al., 2021), underscoring the complex and unique nature of what constitutes health in a community.”
Quantifying metadata relevance to network block structure using description length
Mangold, L., & Roth, C. (2024) · Communications Physics · doi:10.1038/s42005-024-01819-y · Cited by 2
Beers, A. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2430628 · Cited by 4
“For example, Conover et al. (2011) documents aspects of Twitter election publics during the 2010 election cycle, Wang et al. (2012) for the 2012, and many others for future election cycles.”
Riedl, J., Drews, W., & Richter, F. (2024) · Frontiers in Political Science · doi:10.3389/fpos.2024.1509981 · Cited by 2
Impiccichè, P., & Viviani, M. (2024) · ACM Transactions on the Web · doi:10.1145/3707701 · Cited by 2
“Conover et al. [10] employed the Label Propagation algorithm [20] to unveil the political structure of specified retweet and mention networks.”
Maximizing Disagreement and Polarization in Social Media Networks using Double Deep Q-Learning
Zareer, M. N., & Šelmić, R. R. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831299 · Cited by 3
“Algorithmic personalization reinforces cognitive biases, creating filter bubbles and echo chambers, though debate persists on whether this results in actual polarization [3]–[5].”
Ng, K. W., Wendt, N. A., Eshun, J., & Saldanha, E. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82435-7_20 · Cited by 2
Mepham, K., Vörös, A., & Stadtfeld, C. (2025) · Network Science · doi:10.1017/nws.2025.2 · Cited by 3
“A possible measure of this form of polarization is the extent to which individuals are socially tied to others who share their political views (Conover et al., 2011; Guerra et al., 2013).”
InfluentialPolarisation: the new 'superword'. Meanings and current uses in English and Spanish
Requejo, M. D. P., & Romano, M. (2025) · Journal of Corpora and Discourse Studies · doi:10.18573/jcads.137 · Cited by 2
“Since then, researchers have analysed how polarisation is created (McCarty 2021), how it can be slowed down and even prevented (Axel-rod et al. 2021; Simchon et al. 2022), and how it is intensified by social media (Bail et al. 2018; Conover et al. 2011; Gillani et al. 2018; Yardi & Boyd 2010).”
Dynamics of Electoral Polarisation in Climate Policy Discourse: A Temporal Network Analysis
Drecker, P. (2025) · Politics and Governance · doi:10.17645/pag.10004 · Cited by 4
“However, a widely used approach to investigate polarisation on Twitter is examining retweets as an endorsement of the original poster’s message to represent interactions (T. H. Y. Chen et al., 2021; Conover et al., 2011; Darius, 2022; Falkenberg et al., 2022; Kubinec & Owen, 2021).”
InfluentialЗиновьевич, Б. Р., & Алексеевич, Г. И. (2017) · Vestnik Tomskogo gosudarstvennogo universiteta Filosofiya Sotsiologiya Politologiya · doi:10.17223/1998863x/40/21 · Cited by 2
Ахременко, А. С., Филиппов, И. Б., & Yureskul, E. (2020) · International Conference on Engineering Management of Communication and Technology (EMCTECH) · doi:10.1109/emctech49634.2020.9261517 · Cited by 2
“…measures of polarization: for ideological polarization, real-life users often report their partisan beliefs in their profile, while for others ideology can be inferred through content they create an share [3, 23]; for affective polarization, unfriending behaviour can be observed directly over time.”
BackgroundMining Twitter Data for a More Responsive Software Engineering Process
Williams, G., & Mahmoud, A. (2017) · IEEE/ACM International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-c.2017.53 · Cited by 2
“, Twitter, YouTube, and Facebook) and the urgent need for effective methods to analyze such large amounts of limited textual data [2], [5].”
BackgroundTracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014
Potash, P., Romanov, A., Gronas, M., et al. (2017) · doi:10.18653/v1/w17-4203 · Cited by 2
“Such methods have recently been used with success in the context of liberal/conservative biases in US politics (Conover et al., 2011; Zhou et al., 2011; Gamon et al., 2008).”
BackgroundMethodSzabó, G., & Bene, M. (2015) · Socio hu · doi:10.18030/socio.hu.2015.4.67 · Cited by 2
(2002) · Duke University Press eBooks · doi:10.2307/j.ctv1220m0v.7 · Cited by 2
Paschalides, D., Pallis, G., & Dikaiakos, M. D. (2021) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3487351.3489443 · Cited by 4
ERIS: An Approach Based on Community Boundaries to Assess Polarization in Online Social Networks
Guyot, A., Gillet, A., Leclercq, É., & Cullot, N. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-05760-1_6 · Cited by 2
dos Santos, P. D., & Goya, D. (2022) · doi:10.5753/brasnam.2022.223212 · Cited by 2
The Consensus Problem in Polities of Agents with Dissimilar Cognitive Architectures
Sowinski, D., Carroll-Nellenback, J., DeSilva, J. M., Frank, A., Ghoshal, G., & Gleiser, M. (2022) · Entropy · doi:10.3390/e24101378 · Cited by 2
Rains, S. A., Harwood, J., Shmargad, Y., Kenski, K. M., Coe, K., & Bethard, S. J. (2022) · Journal of Communication · doi:10.1093/joc/jqac037 · Cited by 2
Peters, U., & Quintana, I. O. (2024) · Synthese · doi:10.1007/s11229-024-04639-3 · Cited by 1
“…as a key contributor to political polarization, i.e., the affective or ideological division and aversion between people of different political orientations (Conover et al., 2021; Hong & Kim, 2016), investigating the use of generics about political groups on Twitter becomes especially interesting.”
da Silva, L. J., de Araújo, R. M., & Corrêa, A. (2024) · doi:10.5753/brasnam.2024.1965 · Cited by 2
“Estudos apontam que interações políticas no Twitter apresentam fortes padrões polarizados de retweets , demonstrando um alto grau de clustering pela tendência ideológica dos usuários [Conover et. al.”
BackgroundInfluentialCorona, A., & Yáñez, B. A. M. (2019) · INDEX COMUNICACIÓN · doi:10.33732/ixc/09/01activi · Cited by 2
Uchida, A. (2018) · PSYCHOLOGIA · doi:10.2117/psysoc.2019-b012 · Cited by 3
Citizens' Attitude towards Political Corruption and the Impact of Social Media
Mullor, G. B. (2018) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) · doi:10.6035/14102.2018.525941 · Cited by 3
Mind the Gap? Negative Tweets & Partisanship in the House of RepresentativesPreprint
Macdonald, M., & Hua, W. (2020) · doi:10.33774/apsa-2020-qj6hx · Cited by 2
“Many scholars find an increasing climate of polarization online (Conover et al. 2011) that is more negative than it would be offline, as seen through the widespread fragmenting of the online community and non-overlapping social networks (StromerGalley 2014).”
BackgroundHow the Far-Right Polarises Twitter: 'Highjacking' Hashtags in Times of COVID-19Preprint
Darius, P., & Stephany, F. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3709988 · Cited by 2
On Language and Structure in Polarized Communities
Lai, M. (2019) · doi:10.4995/thesis/10251/119116 · Cited by 2
Valladares, T. L., Golino, H. F., & Coan, J. A. (2020) · doi:10.31234/osf.io/u8ghv · Cited by 2
Artificial Intelligence Chatbots Mimic Human Collective BehaviourPreprint
He, J. K., Wallis, F. P. S., Gvirtz, A., & Rathje, S. (2023) · doi:10.31234/osf.io/vf5ap · Cited by 2
Who is on Twitter (“X”)? Identifying demographic of Twitter usersPreprint
Mosleh, M., & Rand, D. G. (2024) · doi:10.31235/osf.io/wxfcz · Cited by 2
López-Robles, A. (2023) · Revista de la Asociación Española de Investigación de la Comunicación · doi:10.24137/raeic.10.19.12 · Cited by 1
Effects of Media Exposure on the Political Polarization Patterns of Students in Pakistan
Bilal, M. Z., Ali, A., & Ullah, S. (2019) · Global Social Sciences Review · doi:10.31703/gssr.2019(iv-iii).38 · Cited by 1
“Selective exposure and diverse exposure go side by side as it was found in previous studies (Conover et al., 2011; Gruzd, 2013; Valentino et al., 2009).”
BackgroundResultWetherell, G. A., Thompson, J. L., Vallejo, I., & Lanning, K. (2023) · Analyses of Social Issues and Public Policy · doi:10.1111/asap.12357 · Cited by 1
Beyond Text: Detecting Image Propaganda on Online Social Networks
Wang, M.-H., & Chen, Y.-L. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3424773 · Cited by 1
“In an extensive study, Conover et al. investigated political communication on Twitter by analyzing a dataset of 250,000 tweets [5].”
A model of Elite interactions and hidden opinions
Shabayek, S. (2025) · Journal of Mathematical Sociology · doi:10.1080/0022250x.2025.2529173 · Cited by 1
“Increased political polarization in society has been widely documented (Adamic & Glance, 2005; Conover et al., 2011; Gentzkow & Shapiro, 2011; Lelkes et al., 2017; Törnberg, 2022), with digital technology often being held responsible (Bail et al., 2018; Pariser, 2011; Sunstein, 2018).”
Fluctuations and extreme events in the public attention on Italian legislative elections
Auconi, A., Federico, L., Riotta, G., & Caldarelli, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-69354-y · Cited by 1
“In particular, studies based on Twitter data elucidated contagion mechanisms in the Arab Spring demonstrations 16 , Occupy Wall Street 17 , and the Spanish “Indignados” 18 .”
Castaños, S. P., Merino, J. A., & Hípola, G. G. (2024) · Frontiers in Political Science · doi:10.3389/fpos.2024.1490834 · Cited by 1
An Analysis of Relationship Between Social Bots and Echo Chambers on X in Modern Japan
Ippa, S., Okubo, T., & Hashimoto, M. (2024) · International Symposium on Computing and Networking · doi:10.1109/candar64496.2024.00014 · Cited by 2
“Reposting on X is used to interact with people who have similar opinions and form clusters [53].”
Polarization and Shared Attention among Influential Amplifiers of 2018 U.S. Primary Candidates
Montez, D., & Shmargad, Y. (2025) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2025.001 · Cited by 1
“While studies of online polarization typically focus on either politicians (e.g., Green et al., 2020) or regular users (e.g., Conover et al., 2011), as far as we know, this is a first attempt at capturing what can be called meso-level polarization among the intermediaries that are central to the…”
Detecção de Posicionamento e Rotulação Automática de Usuários do Twitter: o caso da CPI da Covid-19
dos Santos, P. D., & Goya, D. (2024) · iSys - Brazilian Journal of Information Systems · doi:10.5753/isys.2023.3008 · Cited by 1
Vanin, A. V., Владимировна, Г. О., & Лебедев, А. Н. (2025) · Journal of Modern Foreign Psychology · doi:10.17759/jmfp.2025140109 · Cited by 1
Twitter polarity and computational propaganda: the case of the Woman, Life, Freedom movement in Iran
Rahbari, M. M., Blanchard, P., & Safari, H. (2025) · Journal of Information Technology & Politics · doi:10.1080/19331681.2025.2537270 · Cited by 3
Negative ties highlight hidden extremes in social media polarization
Candellone, E., Babul, S., Togay, Ö., Bovet, A., & Garcia‐Bernardo, J. (2025) · Network Science · doi:10.1017/nws.2025.10006 · Cited by 3
“…information spread plays a key role in facilitating an increased polarization around controversial issues and amplifying political divisions and conflicts ( garimella2017reducing ; adamic2005political ; conover2011political ; hohmannQuantifyingIdeologicalPolarization2023a ;…”
A multilevel network approach to revealing patterns of online political selective exposure
Zhang, Y., Castro, L., Esser, F., & Bovet, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332663 · Cited by 5
“It is a common strategy to study phenomena like selective exposure, polarization, and echo chambers [5,27,59,60].”
Manetti, G., Nitti, C., & Bellucci, M. (2025) · Administration & Society · doi:10.1177/00953997251385229 · Cited by 1
“In our SA, we focused on Twitter (now known as X), a social media platform that is widely used at the international level to convey political opinions publicly and analyze political polarization (Conover et al., 2021; Perdana & Pinandito, 2018; Stieglitz et al., 2014).”
Homophily-induced emergence of biased structures in LLM-based multi-agent AI systems
Mehdizadeh, A., & Hilbert, M. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01535-7 · Cited by 4
“Similarly, another study on Twitter retweet and mention networks during the 2010 U.S. midterm elections and found strong ideological clustering such that retweets overwhelmingly occurred within political communities rather than across them, revealing high political homophily and echo chambers [126].”
Bassolas, A., Massachs, J., Cozzo, E., & Vicens, J. (2025) · Royal Society Open Science · doi:10.1098/rsos.241974 · Cited by 7
“While the retweet network has been previously interpreted as the influence between social media users, other types of interactions, such as mentions, often feature different structural properties [61].”
Agent-based insight into eco-choices: Simulating the fast fashion shift
Soboleva, D. A., & Sánchez, A. (2025) · Ecological Economics · doi:10.1016/j.ecolecon.2025.108824 · Cited by 3
Kamba, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-12801-0_10 · Cited by 1
Enhancing online well-being through transformer-based analysis of misinformation and mental health
Arora, S., Kumar, D., Arora, S., et al. (2026) · Discover Mental Health · doi:10.1007/s44192-026-00385-9 · Cited by 1
Wang, C., Guo, Y., Zhou, R.-T., Li, Y.-J., & Wang, S.-S. (2026) · Online information review (Print) · doi:10.1108/oir-02-2025-0078 · Cited by 2
Wurm, G. J. (2026) · International Journal of Politics Culture and Society · doi:10.1007/s10767-026-09580-6 · Cited by 1
Quintana, A., Guo, Z., Grinberg, N., & Lazer, D. M. J. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009695923 · Cited by 1
Quantifying opinion homophily in online social networks from a bounded confidence perspective
Luan, Y., Ancona, C., Bernardo, C., Pansanella, V., Iudice, F. L., Rossetti, G., Vasca, F., Wu, X., & Altafini, C. (2026) · Communications Physics · doi:10.1038/s42005-026-02760-y · Cited by 2
“For instance, Twitter follow graphs reveal that users cluster tightly along ideological lines 9 , retweet networks form polarized left/right communities 10,11 , and news or content spreadspreferentiallyamonglike-mindedusers 7,12 .”
Propaganda Eleitoral Antecipada: Uma Análise de Postagens em Mídias Sociais
Silva, M., Guimarães, S., Caetano, J. A., et al. (2021) · doi:10.5753/brasnam.2021.16140 · Cited by 1
Empirical Analysis of Aging Effects on Preferential Attachment with a Massive Twitter Dataset
Atwal, K. S., Murphy, J. T., & Garibay, I. I. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685908 · Cited by 1
“Based on the structural properties and interactions among communities of the network over time, [19] demonstrated”
BackgroundExploring the echo chamber concept
Bondi, M., & Sanna, L. (2022) · Discourse approaches to politics, society and culture · doi:10.1075/dapsac.98.07bon · Cited by 1
Co-Activity Maximization in Online Social Networks
Mao, D., Wu, W., & Du, D. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3213260 · Cited by 1
“With the growing popularity of social media [1], online polarization receives ascending attention from researchers in many fields, as this polarization is observed to link to society across many issues in politics [4], [12], [13], public policy [24], and healthcare [14], [25].”
BackgroundCharacterizing Controversiality of Topics Utilizing Eccentricity of Opinions
Pandey, S., & Sayama, H. (2023) · Northeast Journal of Complex Systems · doi:10.22191/nejcs/vol5/iss1/2 · Cited by 3
“[11] Conover, M. et al. “Political Polarization on Twitter”. en.”
InfluentialUribe, J. F. P., Céspedes, L. M. G., & Lombana-Bermúdez, A. (2023) · Reflexión Política · doi:10.29375/01240781.4741 · Cited by 1
“…virtuales pueden entenderse como una comunidad con una orientación temática de fi nida y valores homogéneos en donde se circulan y producen contenidos orientados a rea fi rmar sus ideas, visiones del mundo y con fi rmar sus sesgos (Conover et al., 2011; Dubois & Blank, 2018; Zimmer et al., 2019).”
BackgroundRobinson, J. Y. (2024) · Nordicom review/NORDICOM review · doi:10.2478/nor-2024-0010 · Cited by 1
Partisanship and Risk Talk on Twitter
Zhang, Y., Wong, J. C. S., An, Z., Brimmer, M. J., Joseph, K., & Yang, J. (2024) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2024.009 · Cited by 1
Analyzing Support for U.S. Presidential Candidates in Twitter Polls
Scarano, S., Vasudevan, V., Samory, M., Yang, J., & Grabowicz, P. A. (2024) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2024.icwsm.4 · Cited by 1
“For instance, social media users who are connected and interact tend to live close to each other and be of a similar age (Ugander et al., 2011; Grabowicz et al., 2016) and share political preferences (Conover et al., 2011; Barberá et al., 2015).”
BackgroundAnatomy of elite and mass polarization in social networks
Salloum, A., Chen, T. H. Y., & Kivelä, M. (2025) · Network Science · doi:10.1017/nws.2025.10010 · Cited by 10
“This digital transformation has prompted researchers to increasingly focus on these platforms as rich sources of data for understanding contemporary political dynamics [9, 11, 12, 23].”
Detecting Political Polarization Using Social Media Data
Doğdu, E., Choupani, R., & Sürücü, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61816-1_4 · Cited by 1
The “Non-Musk” effect at X (Twitter)
Zinoviev, D. Y., Sarkar, A., & Bicen, P. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01289-8 · Cited by 4
“Upon applying the Louvain community detection algo - rithm (Blondel et al. 2008), we identified 14 groups of opinion leaders, including highly politically polarized com - munities (Conover et al. 2011).”
Hanke, S., Sicakkan, H. G., Wolleghem, P. G. V., & Heiberger, R. H. (2024) · Nonprofit Policy Forum · doi:10.1515/npf-2023-0126 · Cited by 1
On the Strength of Weak Ties: Barriers to Political Expression Online
Ziv, L., & Yavetz, G. (2025) · Social Sciences · doi:10.3390/socsci14060360 · Cited by 2
“The emergence of homogeneous networks is mainly explained by people’s tendency to connect with others who are similar to them (Aiello et al. 2012; Conover et al. 2011) and thus avoid encountering information that produces cognitive dissonance.”
Schroeder, R., Everton, S. F., & Shepherd, R. (2014) · Lecture notes in social networks · doi:10.1007/978-3-7091-1340-0_10 · Cited by 2
How “VKontakte” Fake Accounts Influence the Social Network of Users
Kaveeva, A., Евгеньевич, Г. К., & Solovyev, V. D. (2018) · Communications in computer and information science · doi:10.1007/978-3-030-02843-5_41 · Cited by 3
A Methodology for the Research of Communicative Strategies of Political Leaders in Online
Иванов, С. А., Legostaeva, N. I., Platonov, K., & Svetlov, K. V. (2020) · Sociology and Law · doi:10.35854/2219-6242-2019-4-6-17 · Cited by 1
Goritz, A., Jörgens, H., & Kolleck, N. (2021) · Revue Internationale des Sciences Administratives · doi:10.3917/risa.874.0725 · Cited by 1
Let Young People Join the Legislative Process. A Twitter Based Experiment on InternshipsPreprint
Barbieri, P. N., Fazio, F., & Gamberini, G. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2576804 · Cited by 1
Do Online Media Polarize? Evidence from the Comments' SectionPreprint
Asker, D., & Dinas, E. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3018223 · Cited by 3
How is Social Media Used in the Context of Complementary and Alternative Medicine? A Scoping ReviewPreprint
Ng, J. Y., Verhoeff, N., & Steen, J. P. (2021) · Research Square · doi:10.21203/rs.3.rs-1048743/v1 · Cited by 1
The Grammar of Politics, through the lens of Surveys and Web-based Social Network methodsPreprint
Karmannaya, E., & de‐Wit, L. (2021) · doi:10.31234/osf.io/v6qx5 · Cited by 1
The Best Paper You'll Read Today: Media Bias and the Public Understanding of SciencePreprint
O’Connor, C., Weatherall, J. O., & Mohseni, A. (2023) · doi:10.31222/osf.io/hpks9 · Cited by 1
Electoral Dynamics, Nationalist Ideology, and Party Positions on Syrian Refugees in TurkeyPreprint
Secen, S., Al, S., & Arslan, B. (2023) · doi:10.31235/osf.io/dbzfm · Cited by 1
Affective Polarization and Dynamics of Information Spread in Online NetworksPreprint
Lerman, K., Feldman, D., He, Z., & Rao, A. (2024) · Research Square · doi:10.21203/rs.3.rs-3855114/v1 · Cited by 2
Verified authors shape X/Twitter discursive communitiesPreprint
Guarino, S., Mounim, A., Caldarelli, G., & Saracco, F. (2024) · Research Square · doi:10.21203/rs.3.rs-4388361/v1 · Cited by 1
THE IMPACT OF INTERNET CONNECTIVITY IN NAVIGATING ONLINE SOCIAL NETWORKS: A CROSS-COUNTRY ANALYSISPreprint
Matlack, A. K., Boots, B. C., & Richardson-Gool, T. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5208556 · Cited by 1
The Role of Online Social Networks in Political Polarization of Elite PoliticiansPreprint
Mousavi, R., Gu, B., & Vinzé, A. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2510676
Age Prediction in Social Networks Based on Word Embedding and Tensor Learning
Lin, Z., & Wang, Y. (2017) · International Conference on Communication and Electronic Information Engineering (CEIE) · doi:10.2991/ceie-16.2017.28
New Media in image-making. How have emerging communications transformed the political process?
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Metadata
- Authors
- Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A.
- Affiliations
- Indiana University (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 89-96
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W4312820073
- Semantic Scholar
- 9a7ae269358c6366cc46dc924d63b47ba405d082
- Cited by
- 626
- References
- 21
- Citations by year
- 2026: 34 · 2025: 66 · 2024: 86 · 2023: 82 · 2022: 65 · 2021: 62
- FWCI
- 107.66
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- political polarization, Twitter, retweets, mentions, social network topology, 2010 midterm elections, political communication, information dissemination, network clustering
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).
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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