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).”
BackgroundFrom Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. A. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,949 · cited 1×
“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 Wealth of Networks: How Social Production Transforms Markets and Freedom
(2006) · Choice Reviews Online · doi:10.5860/choice.44-0999 · Cited by 1,903
Kolbe, R. H., & Burnett, M. S. (1991) · Journal of Consumer Research · doi:10.1086/209256 · Cited by 1,516 · cited 4×
“…annotations agree with an objective judge is Cohen's Kappa, defined as κ = P (α) − P () 1 − P () (2) where P (α) is the observed rate of agreement between annotators , and P () is the expected rate of random agreement given the relative frequency of each class label (Krippendorff 2004; Kolbe 1991).”
BackgroundMethodInfluentialThe law of group polarization.
Sunstein, C. R. (2002) · Journal of Political Philosophy · doi:10.1111/1467-9760.00148 · Cited by 1,371
Beyond Microblogging: Conversation and Collaboration via Twitter
Honeycutt, C., & Herring, S. (2009) · Hawaii International Conference on System Sciences · doi:10.1109/hicss.2009.602 · Cited by 1,055
Beyond Microblogging: Conversation and Collaboration via Twitter
Honey, C., & Herring, S. C. (2009) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2009.89 · Cited by 966
Dynamic debates: an analysis of group polarization over time on Twitter
Yardi, S., & Boyd, D. (2010) · Bulletin of Science, Technology & Society · doi:10.1177/0270467610380011 · Cited by 625 · cited 3×
“Finally, we provide evidence that these network structures result in part from politically motivated individuals annotating tweets with multiple hashtags whose primary audiences consist of ideologically-opposed users, a behavior also documented in the work of Yardi and boyd (2010).”
BackgroundInfluentialAn Omnibus Test of Normality for Moderate and Large Size Samples
D’Agostino, R. B. (1971) · Biometrika · doi:10.1093/biomet/58.2.341 · Cited by 582 · cited 1×
“By testing the sampled modularities with the omnibus K(2) statistic (D’Agostino 1971), we find that this assumption is not true: we have p < 10−10 that variations between the observed data and the best-fit normal distribution are due to random chance.”
MethodThe power and politics of blogs
Farrell, H. J., & Drezner, D. W. (2007) · Public Choice · doi:10.1007/s11127-007-9198-1 · Cited by 567 · 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).”
BackgroundRatkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Cross-ideological discussions among conservative and liberal bloggers
Hargittai, E., Gallo, J., & Kane, M. (2007) · Public Choice · doi:10.1007/s11127-007-9201-x · Cited by 246
Bennett, W. L. (2003) · Contesting Media Power · doi:10.5040/9798216409908.ch-2
Research works printed but not linked: 1
- Sunstein, C. R. 2007. Republic.com 2.0. Princeton Univer- sity Press.
Cited by
Sorted by citations
Exposure to opposing views on social media can increase political polarization
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804840115 · Cited by 1,863
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
“[18], who showed that users frequently produce tweets containing hashtags that table 4.”
BackgroundInfluentialTweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015) · Psychological Science · doi:10.1177/0956797615594620 · Cited by 1,472
“…but, until recently, estimates of individual-level characteristics of social-media users have been calculated only for small, self-selected samples (Conover et al., 2011; Kosinski, Stillwell, & Graepel, 2013) or have focused on variables that are of little direct relevance to the study of…”
BackgroundEmotion shapes the diffusion of moralized content in social networks
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618923114 · Cited by 1,580
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125
“These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52, 40, 39] caused by polarized and segregated online social networks [9, 8], algorithmic popularity bias [45, 20, 37], and other cognitive vulnerabilities such as confirmation bias and motivated reasoning…”
Self-citation
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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