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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Online Bayesian Models for Personal Analytics in Social Media
Volkova, S., & Durme, B. V. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9507 · Cited by 16
“2013a); political orientation (Conover et al. 2011a; Pennacchiotti and Popescu 2011; Zamal, Liu, and Ruths 2012; Cohen and Ruths 2013); personality (Golbeck et al.”
BackgroundWeber, D., Falzon, L., Mitchell, L., & Nasim, M. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00892-x · Cited by 16
“Our analysis found that Supporters used a wide variety of hashtags together, which we speculated was a way to inject the arson counter-narrative into different communities (Conover et al. 2011).”
BackgroundMethodInfluentialEcker-Ehrhardt, M. (2023) · Politics and Governance · doi:10.17645/pag.v11i3.6767 · Cited by 14
Political context of the European vaccine debate on Twitter
Paoletti, G., Dall’Amico, L., Kalimeri, K., Lenti, J., Mejova, Y., Paolotti, D., Starnini, M., & Tizzani, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54863-7 · Cited by 14
“45 ) that retweet networks have high homophily concerning intrinsic characteristics of the nodes, such that users who retweet each other are likely to share the same opinions and are exposed to the same type of content, thus such networks are often called endorsement networks 46 .”
BackgroundA systematic literature review on sentiment analysis techniques, challenges, and future trends
Ali, H. M. U., Farooq, Q., Imran, A., & Hindi, K. M. E. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02365-x · Cited by 20
Romenskyy, M., Spaiser, V., Ihle, T., & Lobaskin, V. (2018) · Royal Society Open Science · doi:10.1098/rsos.171935 · Cited by 16
“Twitter and Facebook) data have been increasingly used to study opinion polarization [29, 30].”
MethodStructure and dynamics of growing networks of Reddit threads
Goglia, D., & Vega, D. (2024) · Applied Network Science · doi:10.1007/s41109-024-00654-y · Cited by 13
“Research on user interactions in online platforms has primarily focused on conflict, controversies, and affective polarization [30, 31, 32, 33, 34], analyzing these social behaviors mostly through sentiment and topic analysis.”
BackgroundStokols, A. (2022) · Urban Geography · doi:10.1080/02723638.2022.2056366 · Cited by 14
“While social media platforms are increasingly seen as a cause of political polarization harmful to democratic traditions (Conover et al., 2011), this study asks (4) how certain digital tools might strengthen neighborhood-based social capital?”
BackgroundTweeting 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
“Figure 4 and Figure 5 are the summary results displaying how strongly the parties ’ general positions on Chapell Hill Expert Survey (CHES) and KONDA 4 surveys correlate with the average PC1 and PC2 scores of the political parties at the two-dimensional representation.”
BackgroundSharing 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
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
“Since hashtag is an important mean for people to participate in political discussion and communication, hashtag usage pattern has also been modeled as feature vectors in many clustering tasks to help learn different user groups (Conover et al., 2011a; Bode et al., 2013, 2015).”
BackgroundMethodMisinformation Beyond the Media: ‘Fake News’ in the Big Data Ecosystem
Salaverría, R., & León, B. (2022) · Studies in big data · doi:10.1007/978-3-030-88028-6_9 · Cited by 13
Measuring user engagement with low credibility media sources in a controversial online debate
Vilella, S., Semeraro, A., Paolotti, D., & Ruffo, G. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00342-w · Cited by 15
“the act of sharing someone else’s tweet, often seen as a form of endorsement (29; 30; 31).”
BackgroundWhose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics
Lee, J., Wu, S., Ertugrul, A. M., Lin, Y., & Xie, L. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19316 · Cited by 13
Algorithmic Distortion of Informational Landscapes
Roth, C. (2019) · Intellectica Revue de l Association pour la Recherche Cognitive · doi:10.3406/intel.2019.1895 · Cited by 13
“…structure (observed early on blogs (Adamic and Glance, 2005) and similarly in micro-blogs (Lietz et al., 2014; Barberá et al., 2015)), where interactions seem to be heavily influenced by homophily (Conover et al., 2011) and selection & influence processes (Lewis et al., 2012), absent algorithms.”
BackgroundThe evolving perception of controversial movies
Amendola, L., Marra, V., & Quartin, M. (2015) · RePEc: Research Papers in Economics · Cited by 13
Proceedings of the Second Workshop on NLP and Computational Social Science
Hovy, D., Volkova, S., Bamman, D., Jurgens, D., O'connor, B., Tsur, O., & Doğruöz, A. S. (2017) · doi:10.18653/v1/w17-29 · Cited by 13
Polarization in Decentralized Online Social Networks
La Cava, L., Mandaglio, D., & Tagarelli, A. (2024) · ACM Web Science Conference · doi:10.1145/3614419.3644013 · Cited by 13
Schwitter, N., Pretari, A., Marwa, W., Lombardini, S., & Liebe, U. (2022) · Frontiers in Sociology · doi:10.3389/fsoc.2022.909458 · Cited by 14
Emergence of simple and complex contagion dynamics from weighted belief networks
Aiyappa, R., Flammini, A., & Ahn, Y. (2024) · Science Advances · doi:10.1126/sciadv.adh4439 · Cited by 13
“It is at the heart of numerous social challenges that our society is facing ( 6 ), including the prevalence of false information ( 7 ), political polarization ( 8 , 9 ), climate change denial ( 10 ), and vaccine hesi-tancy ( 11 , 12 ).”
Self-citationTowards long-term depolarized interactive recommendations
Lechiakh, M., El-Moutaouakkil, Z., & Maurer, A. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103833 · Cited by 12
Allison, K. R., & Bussey, K. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7275 · Cited by 12
“However, research into political discussions online indicates a tendency towards forming echo chambers, with political retweeting largely reflecting partisan segregation (Conover et al. 2011) and commenters on Facebook pages of partisan news organisations preferentially linking to small, largely distinct groups of inf…”
BackgroundThe Coevolution of Social Networks and Cognitive Dissonance
Whitaker, R. M., Colombo, G. B., Turner, L. D., Dunham, Y. C., Doyle, D., Roy, E. M., & Giammanco, C. A. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3090833 · Cited by 12
Evolution of the plandemic communication network among serial participants on Twitter
Xu, Y., Sun, Y., Hagen, L., Patel, M., & Falling, M. (2021) · New Media & Society · doi:10.1177/14614448211050928 · Cited by 12
“When the same Twitter API and list of search keywords are used to extract networks of communication, the mention network is larger, denser, more clustered, and less polarized than the retweet network (Conover et al., 2011; González-Bailón et al., 2014).”
BackgroundUsers’ polarisation in dynamic discussion networks: The case of refugee crisis in Sweden
Kopacheva, E., & Yantseva, V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262992 · Cited by 18
Liu, Z., Luo, C., & Lu, J. (2023) · Information Development · doi:10.1177/02666669221148487 · Cited by 14
“…impelling power of a highly selective media environment, some researchers argue that the Internet creates an intense selective exposure than ever before, leading to closed-loop communication (Conover et al., 2011; Gruzd and Roy, 2014; Hong and Kim, 2016; Levendusky, 2013; Shin and Thorson, 2017).”
Polarization in Social Media: A Virtual Worlds-Based Approach
Jacob, D., & Banisch, S. (2023) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.5170 · Cited by 12
“While previous studies disagree on the presence of polarization in social media, our results show that the innate structure of these platforms mandates analysis of resultant opinion dynamics from different perspectives (Conover et al. 2011; Bakshy et al. 2015; Keijzer & Mäs 2022).”
MethodResultNavarrete, C., Macedo, M. G. D. M., Colley, R., Zhang, J., Ferrada, N., Mello, M. E., Lira, R. C., Bastos-Filho, C. J. A., Grandi, U., Lang, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01755-x · Cited by 14
Bilbao-Jayo, A., & Almeida, A. (2018) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/w18-3513 · Cited by 12
Mousavi, R., & Gu, B. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2666052 · Cited by 12
Liberals Engage With More Diverse Policy Topics and Toxic Content Than Conservatives on Social MediaPreprint
Chang, H.-C. H., Druckman, J. N., Ferrara, E., & Willer, R. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/x59qt · Cited by 13
Adaptive method for following dynamic topics on twitter
Magdy, W., & Elsayed, T. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14540 · Cited by 26
“…task are news monitoring (Magdy, 2013), crisis management (Vieweg et al., 2010), customer feedback detection (Chen at al., 2013), public political polarization analysis (Conover, 2011), election results prediction (Tumasjan et al., 2010), and public healthcare monitoring tools (Ali et al., 2013).”
BackgroundDisparity-driven heterogeneous nucleation in finite-size adaptive networks
Yadav, A., Fialkowski, J., Berner, R., Chandrasekar, V. K., & Senthilkumar, D. V. (2024) · Physical review. E · doi:10.1103/physreve.109.l052301 · Cited by 13
“Beyond the physical systems, phase transitions in complex networks can shed more light on intriguing biological, ecological, and social problems such as population collapse and species extinction [7], polarization in society [8], and crashes in financial markets [9].”
BackgroundGao, X., Roth, E., McKelvey, K., et al. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1905-5_17 · Cited by 11
Self-citationTimeline Analysis of Twitter User
Sujay, R., Pujari, J., Bhat, V. S., & Dixit, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.179 · Cited by 11
Characterizing the Social Media News Sphere through User Co-Sharing Practices
Samory, M., Abnousi, V. K., & Mitra, T. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7327 · Cited by 12
“In particular, several works depict social media audience as divided into conservative and liberal stances (Conover et al. 2011; Garimella et al. 2018; Bakshy, Messing, and Adamic 2015).”
BackgroundTrolling asymmetry toward Republicans and Democrats and the shift from foreign to domestic trolling
Fichman, P., & Akter, S. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.101998 · Cited by 12
Rasmussen, T. (2016) · Universitetsforlaget eBooks · doi:10.18261/9788215027180-2016 · Cited by 14
Crisis de la caravana de migrantes: Algunas realidades sobre el discurso público en Twitter
Toudert, D. E. (2021) · Migraciones internacionales · doi:10.33679/rmi.v1i1.2172 · Cited by 11
“In this sense, as mentioned by Conover et al. (2011), the polarization of the debate on Twitter largely translates to competitive discussions generated within the framework of the political preferences of the debaters.”
BackgroundCheong, H. J., Mohammed-Baksh, S., & Ju, I. (2022) · KOME · doi:10.17646/kome.75672.86 · Cited by 10
Lyu, Z. (2023) · New Media & Society · doi:10.1177/14614448231180654 · Cited by 9
“Although many previous studies have suggested political homophily on social media (Barberá et al., 2015; Conover et al., 2011), the conversation threads of politicians’ tweets appear to involve more interactions with individuals of diverse views.”
Bursts of communication increase opinion diversity in the temporal Deffuant model
Zarei, F., Gandica, Y., & Rocha, L. E. C. (2024) · Scientific Reports · doi:10.1038/s41598-024-52458-w · Cited by 11
Voter model can accurately predict individual opinions in online populations
Vendeville, A. (2025) · Physical review. E · doi:10.1103/physreve.111.064310 · Cited by 10
References to unbiased sources increase the helpfulness of community fact-checks
Solovev, K., & Pröllochs, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-09372-6 · Cited by 19
“Furthermore, the stark polarization among social media users [24, 25] might results in different interpretations of facts or even completely different sets of acknowledged facts [26].”
Online polarization and cross-fertilization in multi-cleavage societies: the case of Spain
Rodríguez-Casañ, R., García-Vidal, E., Grimaldi, D., Carrasco-Farré, C., Vaquer-Estalrich, F., & Vila‐Francés, J. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00909-5 · Cited by 11
Opinion manipulation on Farsi Twitter
Farzam, A., Moradi, P., Mohammadi, S., Padar, Z., & Siegel, A. A. (2023) · Scientific Reports · doi:10.1038/s41598-022-26921-5 · Cited by 15
“Modularity is a commonly used measure for evaluating the degree of community seggregation and potential for polarization in social networks [25, 26].”
Polarization and Non-Positive Social Influence
Li, Z., & Tang, X. (2012) · International Journal of Knowledge and Systems Science · doi:10.4018/jkss.2012070102 · Cited by 10
Aragón, P., Volkovich, Y., Laniado, D., & Kaltenbrunner, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14723 · Cited by 10
“This assumption relies on previous studies of political discussions on social media (Adamic and Glance 2005; Conover et al. 2011).”
BackgroundAlgorithmes de recommandation et culture technique : penser le dialogue entre éducation et design
Claes, A., Wiard, V., Mercenier, H., Philippette, T., Dufrasne, M., BROWET, A., & Jungers, R. M. (2021) · Tic & société · doi:10.4000/ticetsociete.5915 · Cited by 12
Web Archives: The Future(s)Preprint
Meyer, E. T., Thomas, A., & Schroeder, R. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1830025 · Cited by 10
A high-dimensional approach to measuring online polarization
Phillips, S. C., Uyheng, J., & Carley, K. M. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00227-6 · Cited by 13
“Remarkably, most of these methods have only successfully been applied to uni-dimensional networks, typically interaction networks where edges indicate one user re-posts, mentions, or follows another [36–42].”
MethodAgents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks
Lu, H.-C., & Lee, H. (2024) · Social Science Computer Review · doi:10.1177/08944393241270382 · Cited by 9
“Furthermore, homophily is also a well-documented feature in empirical study for information dissemination in online social network (Conover et al., 2011).”
Shehata, A. M. K., Al‐Suqri, M. N., Osman, N. E., Hamad, F. F., Husaini, Y. N. A., & Mahfouz, A. (2024) · IEEE Access · doi:10.1109/access.2024.3518204 · Cited by 14
“The valence score aids in determining a word’s relative importance among false and true messages, according to Conover et al. [57].”
Hiding in Plain Sight: A Tale of Trust and Mistrust inside a Community of Citizen Reporters
Mustafaraj, E., Metaxas, P., Finn, S., et al. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14276 · Cited by 9
“We are grateful to Yesenia Trujillo for manually verifying accounts of Mexican users and translating tweets.”
BackgroundMethodParameterizing network graph heterogeneity using a modified Weibull distribution
Ozbay, S. A., & Nguyen, M. (2023) · Applied Network Science · doi:10.1007/s41109-023-00544-9 · Cited by 9
“Recent attention to graph models have been made in the areas of statistical models [3, 16, 10, 6], social network analysis [17], epidemiological models [14], political polarization models [4], and several other domains.”
BackgroundBodrunova, S. S., Blekanov, I. S., & Kukarkin, M. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-92270-6_56 · Cited by 8
Masiya, T., & Lubinga, S. (2023) · Studies in Media and Communication · doi:10.11114/smc.v11i7.6151 · Cited by 8
“It is important to note that prior studies have shown that social media play a critical role in influencing global political discourse (Conover et al. 2021).”
Laato, S., Inaba, N., & Paloheimo, M. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.324 · Cited by 8
“The findings share a similarity to polarization of peoples’ opinions due to social media and personalized content [67, 49].”
Controlling Segregation in Social Network Dynamics as an Edge Formation Game
Luo, R., Nettasinghe, B., & Krishnamurthy, V. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3162789 · Cited by 8
“For example, [27] demonstrates that the network of political communication on Twitter exhibits a highly segregated partisan structure.”
BackgroundLearning Ideological Embeddings from Information Cascades
Monti, C., Manco, G., Aslay, Ç., & Bonchi, F. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482444 · Cited by 9
“described as echo chambers [19]; these appear in retweet networks as clusters with a homogeneous opinion [7, 14, 16, 26].”
BackgroundDoes Social Media Use Promote Political Mass Polarization?
Ludwig, K., & Müller, P. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748928232-118 · Cited by 10
Resampling reduces bias amplification in experimental social networks
Hardy, M. D., Thompson, B., Krafft, P. M., & Griffiths, T. L. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01715-5 · Cited by 11
Power Laws in Ad Hoc Conflictual Discussions on Twitter
Bodrunova, S. S., & Blekanov, I. S. (2018) · Communications in computer and information science · doi:10.1007/978-3-030-02846-6_6 · Cited by 8
Analyzing the Behaviour of Twitter Bots in Post Brexit Politics
Bello, B. S., & Heckel, R. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931874 · Cited by 9
“This use of social media was reported in [13], [15], [30] and found to be effective in influencing public opinion [2], [4], [12].”
BackgroundMedia Polarization on Twitter During 2019 Indonesian Election
Maulana, A., & Situngkir, H. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-65347-7_55 · Cited by 9
Goritz, A., Jörgens, H., & Kolleck, N. (2021) · International Review of Administrative Sciences · doi:10.1177/00208523211022823 · Cited by 8
“Twitter has become a central platform for political debates at the national and international levels (Conover et al., 2011; Jungherr, 2014).”
BackgroundTransient behavior of gossip opinion dynamics with community structure
Xing, Y., & Johansson, K. H. (2024) · Automatica · doi:10.1016/j.automatica.2024.111627 · Cited by 8
Szabó, G., & Bene, M. (2015) · Intersections · doi:10.17356/ieejsp.v1i1.30 · Cited by 8
“As for the method, we run network analysis (for the application in political science, see: Waugh et al. 2009; Conover et al. 2011).”
MethodWilliams, M. J., Cioroianu, I., & Williams, H. T. P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i2.14843 · Cited by 8
“Analysis of online political networks has repeatedly identified partisan “echo chambers” in which users interact only with likeminded others and are isolated from alternative viewpoints (e.g. (Adamic and Glance 2005; Conover et al. 2011; 2012)).”
BackgroundNuernbergk, C. (2020) · Springer eBooks · doi:10.1007/978-3-658-26242-6_54-1 · Cited by 8
Socializing Alone: How Online Homophily Has Undermined Social Cohesion in the USPreprint
Сергеевич, Е. Р., Petrova, M., Russo, G., & Yanagizawa-Drott, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4738801 · Cited by 8
Park, J., Yang, J., Tolbert, A. N., & Bunsold, K. (2024) · Journal of Information Science · doi:10.1177/01655515241238405 · Cited by 10
RT, Sputnik et le mouvement des Gilets jaunes : cartographie des communautés politiques sur Twitter
Gérard, C., Marotte, G., & Salamatian, L. (2020) · L’Espace Politique · doi:10.4000/espacepolitique.8092 · Cited by 7
Modeling and Control of the Public Opinion: An Agree-Disagree Opinion Model
Bidah, S., Zakary, O., & Rachik, M. (2020) · International Journal of Differential Equations · doi:10.1155/2020/5864238 · Cited by 7
“elections are attracting the polarization in the internet, where blogs and Twitter have a strong political polarization [9, 10].”
BackgroundRealtime user ratings as a strategy for combatting misinformation: an experimental study
Stein, J., Frey, V., & van de Rijt, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-28597-x · Cited by 8
“The level of segregation rarely reaches the extremity implied by the terms ‘echo chambers’ or ‘filter bubbles’ but is nonetheless substantial 34–37 .”
BackgroundIdentifying Political Polarization in Social Media: A Literature Review
Rum, S. N. M., Mohamed, R., & Asfarian, A. (2023) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.34.1.8089 · Cited by 11
Auwal, A. M., & Ersoy, M. (2024) · Media International Australia · doi:10.1177/1329878x241280234 · Cited by 9
“For example, Conover et al. ’ s (2011) analysis of Twitter demonstrates how politically-motivated individuals provoke interaction by injecting partisan content into information streams whose audience consists of ideologically opposed users, suggesting that Twitter has emerged as a viable platform…”
Ahmad, K., Alam, F., Qadir, J., Qolomany, B., Khan, I., Khan, T., Suleman, M., Said, N., Hassan, S. Z., Gul, A., et al. (2022) · JMIR Formative Research · doi:10.2196/36238 · Cited by 7
“We compared the vocabularies of all categories using the valence score [30,31], θ for every token, x, using the following equation:”
Análise de Propagandas Eleitorais Antecipadas no Twitter
Guimarães, S., Silva, M., Caetano, J. A., de Araújo, M., Santos, J. F. D. S., Reis, J. C. S., da Silva, A. P. C., Benevenuto, F., & Almeida, J. M. (2022) · Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) · doi:10.5753/brasnam.2022.222602 · Cited by 7
Misinformation on Trial: Media Coverage of a Murder, Public Conversation and Fact-Checking
Vázquez-Herrero, J., Negreira-Rey, M.-C., & García, X. L. (2023) · Journalism Practice · doi:10.1080/17512786.2022.2164328 · Cited by 11
“Thus, users with a strong political motivation tend to interact on Twitter by disseminating partisan content in information streams dominated by an audience with an opposing ideology (Conover et al. 2021).”
BackgroundMining twitterspace for information: Classifying sentiments programmatically using Java
Fiaidhi, J., Mohammed, O. A., Mohammed, S., et al. (2012) · International Conference on Digital Information Management · doi:10.1109/icdim.2012.6360089 · Cited by 7
Agenda Diversity in Social Media Discourse: A Study of the 2012 Korean General Election
Park, S., Ko, M., Lee, J., & Song, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14398 · Cited by 6
“Keyword cue-based collection (e.g., hashtags or trending terms) is often used (Conover et al. 2011; Starbird and Palen 2012) to identify popular issues and collect related tweets; however, our interest was to have an overview of the discourse rather than to focus on a number of selected popular…”
BackgroundDivided They Tweet? A Comparative Analysis of Twitter Networks of Pro- and Anti-EU Parties
Heft, A., Wittwer, S., & Pfetsch, B. (2017) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-137-59643-7_9 · Cited by 7
Detecting Malicious Social Robots with Generative Adversarial Networks
Wu, B., Liu, L., Dai, Z., Wang, X., & Zheng, K. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.11.018 · Cited by 7
“The primary objectives of these social robots are to create the illusion that a social network very actively to influence public opinion [1], to be employed as a means of political penetration [2], and to spread malicious content.”
BackgroundTarget-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis
Li, X., Chen, W. R., Wang, T., & Huang, W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63564-4_5 · Cited by 9
Marcos-García, S. (2018) · doi:10.6035/14111.2018.253296 · Cited by 7
How the Far-Right Polarises Twitter: 'Highjacking' Hashtags in Times of COVID-19Preprint
Darius, P., & Stephany, F. (2020) · doi:10.31235/osf.io/n6f3r · Cited by 7
Bodrunova, S. S., Blekanov, I. S., & Tarasov, N. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35915-6_13 · Cited by 6
Heavy users fail to fall into filter bubbles: evidence from a Chinese online video platform
Fu, C., Che, Q., Li, Z., Yuan, F., & Min, Y. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1423851 · Cited by 8
Zhang, C., Wang, X., Min, Y., Yu, S., Wu, Y., Xuan, Q., & Fu, C. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130487 · Cited by 8
Social media campaigning and voter behavior–evidence for the German federal election 2021
Aziz, A. I., & Bischoff, I. (2025) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2025.102685 · Cited by 6
Volchenkov, D., & Putkaradze, V. (2025) · Mathematics · doi:10.3390/math13101625 · Cited by 6
Bouchaud, P. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00255-w · Cited by 7
“…impressed in the timelines maximizing engagement, the ones belonging to the convex hull of previously engaged authors are 𝜌 ( 𝜙 = 1 ) = 43.3 % [ 40.9,45.4 ] more numerous, with respect to the friends having published tweets (for reverse chronological timelines ( 𝜙 = 0 ) = 8.9 % [ 8.4,9.6 ]…”
InfluentialNeural discovery of balance-aware polarized communities
Gullo, F., Mandaglio, D., & Tagarelli, A. (2024) · Machine Learning · doi:10.1007/s10994-024-06581-4 · Cited by 6
“In the past few years, we have witnessed a plethora of studies about polarization on social media (Conover et al., 2011; Hohmann et al., 2023; Garimella et al., 2017).”
#FrauenSagenNein - bridging the divide: analyzing the affective network of gender-critical alliances
Zahn, P. H., & Lünenborg, M. (2024) · Feminist Media Studies · doi:10.1080/14680777.2024.2400497 · Cited by 9
Echo chamber formation sharpened by priority users
Ferraz de Arruda, H., Andrade Oliveira, K., & Moreno, Y. (2024) · iScience · doi:10.1016/j.isci.2024.111098 · Cited by 6
Pena, C. B., MacCarron, P., & O’Sullivan, D. J. P. (2025) · Royal Society Open Science · doi:10.1098/rsos.240454 · Cited by 8
Determining the Presence of Political Parties in Social Circles
Gysel, C. V., Goethals, B., & de Rijke, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14650 · Cited by 6
Change My Mind: Data Driven Estimate of Open-Mindedness from Political Discussions
Pansanella, V., Morini, V., Squartini, T., & Rossetti, G. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_8 · Cited by 6
“One of the most debated and analyzed phenomena on online social networks is the tendency to observe political polarization [5,19,1,20], i.e., the divergence of political attitudes to ideological extremes not aiming at reaching any form of synthesis.”
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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