The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index; 5 of the 22 sources its claims cite were checked for support.
Quantifying controversy on social media
Open accessAbstract
Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view but also allows the filtering and aggregation of social media content for disseminating news stories. In this article, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media. Unlike previous work, rather than studying controversy in a single hand-picked topic and using domain-specific knowledge, we take a general approach to study topics in any domain . Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, (ii) partitioning the conversation graph to identify potential sides of the controversy, and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, different graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and show that content features are vastly less helpful in this task.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper presents a domain-agnostic, graph-based pipeline for quantifying controversy on social media by building conversation graphs, partitioning them into two sides, and scoring how separated those sides are. Across Twitter topics and external datasets, a new random-walk-based measure (RWC) best captures controversy, while content-only features are largely unhelpful.
Key findings
- The proposed Random Walk Controversy (RWC) measure most cleanly separates controversial from non-controversial topics; baselines GMCK and MBLB “fail to separate the two groups,” especially on retweet graphs.
- An efficient RWC variant using random walk with restart is almost identical to Monte Carlo RWC (Pearson’s r = 0.96) and up to 200 times faster.
- Content-based graph building and bag-of-words features do not reliably distinguish controversial topics; an unpaired Wilcoxon rank-sum test at p = 0.05 could not reject the null of no difference. Sentiment variance is more useful: controversial topics have variance at least 2, non-controversial at most 1.5.
- On external datasets, all measures distinguish controversial graphs to some extent except Karate club; Political blogs is judged only moderately controversial, while Twitter politics is among the most controversial. In evolving Venezuela data, RWC and EC correlate with MBLB at r = 0.944 and 0.949, BCC at r = 0.743, and GMCK at r = 0.542.
Methods
- Design: a three-stage pipeline — (i) build a conversation graph for a topic, (ii) partition it into two sides using METIS, and (iii) compute a controversy measure from graph characteristics.
- Data: Twitter hashtags trending Feb 27–Jun 15, 2015, with 10 manually chosen controversial and 10 non-controversial topics; topics expanded from seed hashtags via a co-occurrence similarity measure. Graphs include retweet and follow graphs, plus content and hybrid variants. External datasets from prior work, a 56-day Venezuela dataset, synthetic Erdős-Rényi graphs with planted communities, and a three-month deployment over 924 trending hashtags were also used.
- Analysis: compares RWC, BCC, EC, and baselines GMCK and MBLB using beanplots, correlations, simulations, and external-dataset validation; also tests user-level controversy scores against BiasWatch.
Limitations
- Stated: findings are mostly on Twitter; controversial topics were manually picked, which may introduce bias; only 20 topics are used in the main experiment, risking overfitting; the pipeline relies on graph partitioning; multisided controversies are deferred; evaluation depends on subjective labeling of controversy.
- Stated: sentiment analysis relies on tools that do not work reliably for non-English languages, so it cannot be applied to topics such as #russia_march.
- Evident from the text: content-based approaches produced sparse graphs and overlapping partitions, and several negative results (mentions graph, cuts, modularity, SPID) are reported, indicating the method’s scope is limited to the graph-structural pipeline that worked.
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Claims the paper makes
5 of 22 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy”
- Also stated in: § Paper · plain
“Section 8 shows that our method outperforms existing ones [27,40].”
§ antifying Controversy on Social Media > Abstract
What the sources say
- Could not verifycites [27] sourceExperimental — accuracy not yet measured
The Performance Optimization > Comparison with other Algorithms section compares graph-layout algorithms and measures layout quality, not controversy. Searches for the random-walk-based controversy measure and its claimed advantage found no passage addressing either point; page numbers were unavailable in our copy. (LLM, conf=0.78)
- Not matchedcites [40]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Paper · plain
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
“For all measures the median score of controversial topics is higher than for non-controversial ones.”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
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
“BCC and EC show varying degrees of separation and overlap”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
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
“all the measures agree that it presents only a moderate level of controversy.”
§ antifying Controversy on Social Media > 8.2 External datasets
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“By manual inspection we find that most trending hashtags are not related to controversial discussions [19].”
§ antifying Controversy on Social Media > 4 GRAPH BUILDING
What the sources say
- Not checked — no full text of the sourcecites [19] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The κ value is 0.35, which represents a 'fair' level of agreement, according to Landis and Koch [36].”
§ antifying Controversy on Social Media > 7.1 Comparison with BiasWatch
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We remark that, in preliminary experimentation with this approach, building the retweet graph with a threshold τ = 1 did not produce reliable results.”
§ antifying Controversy on Social Media > 1. Retweet graph.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All the measures are able to distinguish controversial graphs to some extent, in the sense that they return higher values for the controversial cases.”
§ antifying Controversy on Social Media > 8.2 External datasets
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, it also generalizes well as it agrees with previously-defined measures when tested on datasets from existing work.”
§ antifying Controversy on Social Media > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The two baselines GMCK and MBLB instead fail to separate the two groups.”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
- Probably not a substantial claim. 28
- It may state two separate claims as one. 29
- The quote only partly states this claim. 4
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In the paper
“Our methods for identifying controversial topics can be used as building blocks for designing such systems to reduce controversy on social media [21,22] by connecting social media users with content outside their own bubbles.”
§ antifying Controversy on Social Media > 10.3 Conclusions
What the sources say
- Not checked — no full text of the sourcecites [21] source
- Partly supportedcites [22] sourceExperimental — accuracy not yet measured
The Introduction describes methods for measuring controversy and identifying opposing sides, and the Conclusion calls reducing polarization a research-worthy problem. The paper does not say these methods can be used to design systems that reduce controversy by connecting users with content outside their bubbles. Page numbers are not available in our copy. (LLM, conf=0.82)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Several studies [11,42] argue that a democracy needs deliberation, and polarization enable such a deliberation to happen in the public, to a certain extent, thus informing people about the issues and arguments from different sides.”
§ antifying Controversy on Social Media > 10.3 Conclusions
What the sources say
- Not checked — no full text of the sourcecites [11] source
- Could not verifycites [42] sourceExperimental — accuracy not yet measured
The Introduction (p. 1) discusses communities in networks, and the Examples section (p. 7) uses politically classified books to illustrate a network layout. Neither addresses whether democracy needs deliberation or whether polarization enables public deliberation; searches for those concepts found no relevant passage. (LLM, conf=0.78)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The work by Guerra et al. [27] presents clear arguments and examples of why modularity should be avoided.”
§ antifying Controversy on Social Media > 10.2 Negative results
What the sources say
- Could not verifycites [27] sourceExperimental — accuracy not yet measured
The Introduction and Anatomy of ForceAtlas2 discuss using modularity to interpret network layouts, but do not discuss Guerra et al. or give arguments and examples for avoiding modularity. Searches for Guerra and for arguments that modularity should be avoided found no supporting passage in our copy. (LLM, conf=0.73)
Strength:boosted· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Previous studies have also shown that people retweet similar ideologies but mention across ideologies [5].”
§ antifying Controversy on Social Media > 10.2 Negative results
What the sources say
- Could not verifycites [5] sourceExperimental — accuracy not yet measured
The Conclusions (page unavailable) discuss polarization in Americans’ political views and abortion attitudes, not how people interact on social media. Searches for retweets, mentions, and interactions across ideologies found no passage in our copy addressing the claimed pattern. (LLM, conf=0.76)
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
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
“As previously stated, we omit results for the content and hybrid graph building approaches presented in Section 4, as they do not perform well.”
§ antifying Controversy on Social Media > 8 EXPERIMENTS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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
“Both RWC and MBLB report low controversy scores for this graph.”
§ antifying Controversy on Social Media > 8.2 External datasets
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference among these methods is not significant, however from visual inspection METIS generates the cleanest partitions.”
§ antifying Controversy on Social Media > 5 GRAPH PARTITIONING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“From an initial visual inspection of the partitions identified on retweet and follow graphs, we find that the partitions match well with our intuition of which topics are controversial (the partitions returned by METIS are well separated for controversial topics).”
§ antifying Controversy on Social Media > 5 GRAPH PARTITIONING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“although EC performs slightly better as the distributions are more concentrated”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
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
“Especially on the retweet graph, the two groups are almost indistinguishable.”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
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
“Only the largest connected component is shown in the visualization, though in all the cases the largest connected component contains more than 90% of nodes.”
§ antifying Controversy on Social Media > 5 GRAPH PARTITIONING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We omit the comparison with RW C user scores as they are almost identical to the ones by BiasWatch.”
§ antifying Controversy on Social Media > 7.1 Comparison with BiasWatch
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the contrary, the partitions identified on content graphs fail to match our intuition.All three variants of the content-based approach lead to sparse graphs and highly overlapping partitions, even in cases of highly controversial issues.”
§ antifying Controversy on Social Media > 5 GRAPH PARTITIONING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In most of the experiments presented so far, we hand-picked known topics which are controversial and showed that our method is able to separate them from the non-controversial topics.”
§ antifying Controversy on Social Media > 8.5 Controversy detection in the wild
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. 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
“All measures however present a dip on day 'D', an increase in controversy in the second half, and another dip on day 'D+20'.”
§ antifying Controversy on Social Media > 8.3 Evolving controversy
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The measure proposed in the original paper, MBLB, which we use as 'ground truth', shows a clear decrease of controversy on the day of the death, followed by a progressive increase in the controversy of the conversation.”
§ antifying Controversy on Social Media > 8.3 Evolving controversy
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“#whosiburningblackchurches (score 0.332): A hashtag about the burning of predominantly black churches.”
§ antifying Controversy on Social Media > 8.5 Controversy detection in the wild
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
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
“#nationalfriedchickenday (score 0.393): A debate between meat lovers and vegetarians about the ethics of eating meat.”
§ antifying Controversy on Social Media > 8.5 Controversy detection in the wild
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“#communityshield (score 0.314): Discussion between the fans of two sides of a soccer game.”
§ antifying Controversy on Social Media > 8.5 Controversy detection in the wild
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
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 find that both assortativity and modularity (which have been previously used to identify controversy) do not correlate with the controversy scores, and are not good predictors for how controversial a topic is.”
§ antifying Controversy on Social Media > 10.2 Negative results
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe a clear pattern: the RWC score increases as we increase the density within the communities, and decreases as we add noise to the community structure.”
§ antifying Controversy on Social Media > 8.4 Simulations
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have shown that previously-used measures are not reliable and demonstrated that controversy can be identified both in the retweet and topic-induced follow graph.”
§ antifying Controversy on Social Media > 10.3 Conclusions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Controversial ones have a variance of at least 2, while non-controversial ones have a variance of at most 1.5.”
§ antifying Controversy on Social Media > 9.2 Sentiment analysis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“RWC is close to one when the probability of crossing sides is low, and close to zero when the probability of crossing sides is comparable to that of staying on the same side.”
§ antifying Controversy on Social Media > 6.1 Random walk
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt 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
“which takes values between zero and one.”
§ antifying Controversy on Social Media > 6.6 Dipole Moment
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To avoid potential overfitting, we use only eight graphs as testbed during the development of the measures, half of them controversial (beefban, nemtsov, netanyahu, russia_march) and half non-controversial (sxsw, germanwings, onedirection, ultralive).”
§ antifying Controversy on Social Media > 8 EXPERIMENTS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“EC is close to one for controversial topics, corresponding to better-separated graphs and thus to higher degree of controversy, and close to zero for non-controversial topics.”
§ antifying Controversy on Social Media > 6.4 Embedding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If indeed there are two sides which do not agree with each other -a controversy-then the two partitions should be loosely connected to each other, given the semantic of the edges.”
§ antifying Controversy on Social Media > 3.2 Partitioning the Graph
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our previous Monte Carlo sampling does not take into consideration such graph structure, and the direction of information propagation, as the random walk process needs to be made ergodic for the sampling process to function.”
§ antifying Controversy on Social Media > 6.2 An efficient variant of the random walk controversy score
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Retweet graphs are inherently directed.
- Rests on an assumption: Directed retweet graphs are very often star-like.
- Rests on an assumption: There are a few authoritative users who generate information that spreads through the graph.
In the paper
“Again, our methods are not only more general and domain agnostic, but they provide more intuitive results.”
§ antifying Controversy on Social Media > 2 RELATED WORK
What the sources say
- Not matchedcites Morales et al. [40]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Not matched
In the paper
“Again, our methods are not only more general and domain agnostic, but they provide more intuitive results.”
§ antifying Controversy on Social Media > 2 RELATED WORK
What the sources say
- Not matchedcites Morales et al. [40]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Not matched
In the paper
“The κ value is 0.35, which represents a 'fair' level of agreement, according to Landis and Koch [36].”
§ antifying Controversy on Social Media > 7.1 Comparison with BiasWatch
What the sources say
- Not matchedcites Landis and Koch [36]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A 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
“which assumes values close to zero when the divergence is small, and close to one when the divergence is large.”
§ antifying Controversy on Social Media > 6.3 Betweenness
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This result suggests that both graph building methods, retweet and follow, are able to capture the difference between controversial and non-controversial topics.”
§ antifying Controversy on Social Media > 8.1 Twi er hashtags
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The topics in Table 2 are correctly labeled as controversial or non-controversial.
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
“Even though we do not make use of any content information in our measure, we perform at par”
§ antifying Controversy on Social Media > BiasWatch
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this section, we propose a variant of RWC defined as a special case of a random walk with restartthus leading to a much more efficient computation.”
§ antifying Controversy on Social Media > 6.2 An efficient variant of the random walk controversy score
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearThe quote only partly states this claim.
- Rests on an assumption: Collecting a large number of samples is computationally intensive, and leads to slow evaluation of RWC.
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
“Conversely, BCC is able to capture the desired behavior, which suggests that shortest-path and random-walk based measures might have a complementary function.”
§ antifying Controversy on Social Media > 8.2 External datasets
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we take a general approach to study topics in any domain.”
- Also stated in: § 1 INTRODUCTION · plain
“We develop a framework to identify controversy regarding topics in any domain (e.g., political, economical, or cultural), and without prior domain-specific knowledge about the topics in question.”
§ antifying Controversy on Social Media > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1 INTRODUCTION · plain
In the paper
“BiasWatch [37] is a recently-proposed, light-weight approach to compute controversy scores for users on Twitter.”
§ antifying Controversy on Social Media > 7.1 Comparison with BiasWatch
What the sources say
- Not checked — no full text of the sourcecites [37] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A 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
“These methods are not comparable, meaning, a score of 0.5 for RWC is not the same as a 0.5 for BCC.”
§ antifying Controversy on Social Media > 8.2 External datasets
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“Conover et al. [10] rely on the mention graph in Twitter to detect controversies.”
§ antifying Controversy on Social Media > 10.2 Negative results
What the sources say
- Not checked — no full text of the sourcecites [10] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Our evaluation is based on our intuitive labelling that a topic is controversial/polarized.”
§ antifying Controversy on Social Media > Evaluation.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- The authors state this as an assumption: The authors' intuitive labelling of topics as controversial/polarized is reasonable for developing methods that can be adapted to large-scale systems.
- Rests on an assumption: Ground truths for controversial topics are hard to find, especially for ephemeral issues.
- Rests on an assumption: The hashtags represent the intuitive notion of controversy that the authors strive to capture.
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
“While this signal is clear, it is not straightforward to incorporate it into the measures based on graph structure.”
§ antifying Controversy on Social Media > 9.2 Sentiment analysis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. 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
“As such, these methods do not fit in the pipeline described in Section 3.”
§ antifying Controversy on Social Media > 9 CONTENT
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Prior work identifies controversies by comparing the structure of the graph with randomly permuted ones [10].”
§ antifying Controversy on Social Media > 10.2 Negative results
What the sources say
- Not checked — no full text of the sourcecites [10] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Our pipeline relies on a graph partitioning stage, whose quality is fundamental for the proper functioning of the controversy measures.”
§ antifying Controversy on Social Media > Overfitting.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Graph partitioning is a hard but well studied problem.
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
“We manually pick the controversial topics in our dataset, which might introduce bias.”
§ antifying Controversy on Social Media > Choice of data.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“There are many ways to define what is controversial, depending on the context, subject and field of study, e.g.See [6] for around a dozen ways to define polarization.”
§ antifying Controversy on Social Media > Evaluation.
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The insight we can draw from these results is that our methods are able to discern a controversial topic from a non-controversial one consistently, irrespective of the domain, and are able to do so more reliably than existing methods (GMCK and MBLB).”
§ antifying Controversy on Social Media > 8.2 External datasets
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“placing a link on a blog post arguably consumes more mental resources than clicking on the retweet button.”
§ antifying Controversy on Social Media > 8.2 External datasets
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The principles behind our measures neatly generalize to multisided controversies.”
§ antifying Controversy on Social Media > Multisided controversies.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Not all controversies involve only two sides with opposing views; some discussions are multifaceted or have three or more competing views.
In the paper
“Guerra et al. need to manually distinguish three different partitions in the graph: gun rights advocates, gun control supporters, and moderates.”
§ antifying Controversy on Social Media > 8.2 External datasets
What the sources say
- Not matchedcites Guerra et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A 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 conclude that our measures generalize well also to the case of evolving graphs, and behave as expected in response to high-impact events.”
§ antifying Controversy on Social Media > 8.3 Evolving controversy
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
Controversial and polarized topics induce conversation graphs with clustered structure, representing different opinions and points of view.
“In particular, we assume that controversial and polarized topics induce graphs with clustered structure, representing different opinions and points of view.”
The authors state this as an assumption.
Users can be split into two sides according to their point of view on a topic.
“assuming that users are split into two sides according to their point of view on the topic, which are these two sides?”
The authors state this as an assumption.
There is homophily in the social network, so on a given topic people agree more often than not with people they follow.
“The rationale for using this graph is based on an assumption of the presence of homophily in the social network, which is a common trait in this setting.”
The paper relies on this without support.
Retweets typically indicate endorsement.
“Retweets typically indicate endorsement.5 Users who retweet signal endorsement of the opinion expressed in the original tweet by propagating it further.”
The authors state this as an assumption.
A single retweet on a topic is not enough of a signal to infer endorsement.
“We presume that a single retweet on a topic is not enough of a signal to infer endorsement.”
The paper relies on this without support.
The quality of the topic affects critically the entire pipeline.
“Since the quality of the topic affects critically the entire pipeline, we want to avert this issue and ensure minimal noise is introduced in the expanded set of hashtags.”
The paper relies on this without support.
The 10 manually selected hashtags represent controversial topics of discussion.
“We first manually pick a set of 10 hashtags that we know represent controversial topics of discussion.”
The paper relies on this without support.
In a controversial discussion, there are authoritative users on both sides, as evidenced by a large degree in the graph.
“It is based on the rationale that, in a controversial discussion, there are authoritative users on both sides, as evidenced by a large degree in the graph.”
The paper relies on this without support.
High degree is a proxy for authoritativeness, as it means that a user has received a large number of endorsements on the specific topic.
“High degree is a proxy for authoritativeness, as it means that a user has received a large number of endorsements on the specific topic.”
The paper relies on this without support.
Retweet graphs are inherently directed.
“retweet graphs (one of our main considerations in this paper) are inherently directed”
The paper relies on this without support.
Directed retweet graphs are very often star-like.
“since these directed graphs are very often star-like”
The paper relies on this without support.
There are a few authoritative users who generate information that spreads through the graph.
“there are a few authoritative users who generate information that spreads through the graph.”
The paper relies on this without support.
Extreme values of R (close to -1 or 1) correspond to users who belong most clearly to either side of the controversy.
“Intuitively, extreme values of R (close to -1 or 1) correspond to users who belong most clearly to either side of the controversy.”
The paper relies on this without support.
Collecting a large number of samples is computationally intensive, and leads to slow evaluation of RWC.
“Nevertheless, collecting a large number of samples is computationally intensive, and leads to slow evaluation of RWC.”
The authors state this as an assumption.
When computing RWC_user(u,X), user u belongs to side X of the controversy.
“Assuming that user u belongs to side X of the controversy (i.e., u ∈ X )”
The paper relies on this without support.
The human annotations for controversy score provided by the authors of [37] are a valid ground truth for controversy.
“The authors also provide datasets which contain human annotations for controversy score (in the range [-2,2]) for 500 randomly selected users.”
The paper relies on this without support.
The external datasets used in previous studies [1,10,27] have a known ground truth for controversy.
“we test all the measures on a set of external datasets used in previous studies [1,10,27] to validate the measures against a known ground truth.”
The paper relies on this without support.
The topics in Table 2 are correctly labeled as controversial or non-controversial.
“The beanplot is divided into two groups, one for controversial topics (left/dark) and one for non-controversial ones (right/light).”
The authors state this as an assumption.
The two sides of a controversial topic use different vocabularies in reference to the issue at hand, despite sharing the hashtag.
“The underlying assumption is that the two sides, while sharing the use of the hashtag for the topic, use different vocabularies in reference to the issue at hand.”
The authors state this as an assumption.
The authors' intuitive labelling of topics as controversial/polarized is reasonable for developing methods that can be adapted to large-scale systems.
“This might not always be true, but given that the alternative is to hand-label/survey the thousands of users, we presume that this assumption is reasonable for developing methods that can be adapted to large scale systems.”
The paper relies on this without support.
Ground truths for controversial topics are hard to find, especially for ephemeral issues.
“Unfortunately, ground truths for controversial topics are hard to find, especially for ephemeral issues.”
The paper relies on this without support.
Twitter is one of the main venues for online public discussion and one of the few for which data is available.
“Twitter is one of the main venues for online public discussion, and one of the few for which data is available.”
The paper relies on this without support.
Graph partitioning is a hard but well studied problem.
“Given that graph partitioning is a hard but well studied problem, we rely on off-the-shelf techniques for this step.”
The paper relies on this without support.
Not all controversies involve only two sides with opposing views; some discussions are multifaceted or have three or more competing views.
“Not all controversies involve only two sides with opposing views.Some times discussions are multifaceted, or there are three or more competing views on the field.”
The paper relies on this without support.
Sentiment analysis technologies do not work reliably for languages other than English.
“Moreover, this feature relies on technologies that do not work reliably for languages other than English and hence cannot be applied for topics such as #russia_march.”
The paper relies on this without support.
The hashtags represent the intuitive notion of controversy that the authors strive to capture.
“Moreover, the hashtags represent the intuitive notion of controversy that we strive to capture, so human judgement is an important ingredient we want to use.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“they confirm the presence of echo chambers (i.e., users with different opinions tend not to follow each other), but only when the topic is controversial.”
From No echo in the chambers of political interactions on Reddit (2021) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“RWC [12, 15] is a widely-used structural polarization measure that informs the present work.”
From DSP: A Statistically-Principled Structural Polarization Measure (2026) · cites this paper as [12]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“At its core, RWC measures the relative exposure of a user to influential members of their own community versus those of an opposing community.”
From DSP: A Statistically-Principled Structural Polarization Measure (2026) · cites this paper as [12]
In the citing paper
“Previous work studied polarization through network analysis or by exploring topics known to be polarized (39). These methodologies have several advantages (40, 41) but can be computationally expensive, create a barrier for adoption for behavioral scientists who lack the required technical expertise, and are most likely context-dependent which can undercut replicability (42).”
From Troll and divide: the language of online polarization (2022) · cites this paper as [40]
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.
Hitting-time percentile interpretation is reversed
MajorThe defined ρ X (u) counts vertices that reach X + in fewer steps than u does. A vertex very close to X + relative to most others would therefore have ρ X (u) near 0, not near 1; the analogous claim for a vertex far from Y + is reversed as well.
“We define ρ X (u) as the fraction of vertices ∈ V with l X < l X u .We define ρ Y (u) similarly.”
“Following this definition, a vertex that, compared to most other vertices, is very close to high-degree vertices X + will have ρ X (u) ≈ 1; and if the same vertex is very far from high-degree vertices Y + , it will have ρ Y (u) ≈ 0”
User controversy score cannot take the stated negative values
MajorThe stated formula is a ratio of nonnegative probabilities, so it can only produce values from 0 to 1. It cannot assign negative scores to one side or use 0 as the neutral midpoint as claimed.
“We denote this score as a real number that takes values in [-1, 1], with 0 representing a neutral score, and ±1 representing the extremes for each side.”
“RW C user (u, X ) = Pr[start = u | end = X + ] Pr[start = u | end = X + ] + Pr[start = u | end = Y + ] . (14”
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“1, we refer the interested reader to [35] for discussion of alternative measures of polarization.”
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Persistent interaction patterns across social media platforms and over time
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“They observed that, as the conflict intensifies, the RWC and the standard deviation of the overall sentiment expressed by the opposing groups are positively correlated and increase in unison.)”
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Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
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Asri, I. E., Kerzazi, N., Uddin, G., Khomh, F., & Idrissi, M. A. J. (2019) · Information and Software Technology · doi:10.1016/j.infsof.2019.06.005 · Cited by 51
“The analysis of controversy has proved useful in social media, such as to detect fake news [55] .”
BackgroundEcho Chambers on Social Media: A comparative analysisPreprint
Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09603 · Cited by 58
Self-citationFighting False Information from Propagation Process: A Survey
Sun, L., Rao, Y., Wu, L., Zhang, X., Lan, Y., & Nazir, A. (2022) · ACM Computing Surveys · doi:10.1145/3563388 · Cited by 58
Exploiting Open Data to analyze discussion and controversy in online citizen participation
Iván Cantador, A., Cortés-Cediel, M. E., & Fernández, M. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102301 · Cited by 50
“Examples of metrics of this type are those proposed by Popescu and Pen-nacchiotti (2010), Rad and Barbosa (2012), Lo et al. (2013) and Garimella et al. (2018).”
MethodEmbeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey
Rashed, A. A.-A., Kutlu, M., Darwish, K. M., Elsayed, T., & Bayrak, C. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18082 · Cited by 49
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop
Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1481 · Cited by 53
Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
Morini, V., Pollacci, L., & Rossetti, G. (2021) · Applied Sciences · doi:10.3390/app11125390 · Cited by 58
“Most of the previous approaches [19,20,48] rely on the follow or friend relationship to define the connections among users.”
BackgroundEcho chamber detection and analysis
Villa, G., Pasi, G., & Viviani, M. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00779-3 · Cited by 56
Emergence of Polarization in Coevolving Networks
Liu, J., Huang, S., Aden, N. M., Johnson, N. F., & Song, C. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.037401 · Cited by 46
Echo Chambers in Online Social Networks: A Systematic Literature Review
Mahmoudi, A., Jemielniak, D., & Ciechanowski, L. (2024) · IEEE Access · doi:10.1109/access.2024.3353054 · Cited by 45
Tubes and bubbles topological confinement of YouTube recommendations
Roth, C., Mazières, A., & Menezes, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231703 · Cited by 44
Jeon, Y., Kim, B., Xiong, A., Lee, D., & Han, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479859 · Cited by 45
Affective Polarization in Online Climate Change Discourse on Twitter
Tyagi, A., Uyheng, J., & Carley, K. M. (2020) · 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381419 · Cited by 41
Effect of Disinformation Propagation on Opinion Dynamics: A Game Theoretic Approach
Guo, Z. Y., Valinejad, J., & Cho, J.-H. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3181130 · Cited by 40
Jiang, J., Ren, X., & Ferrara, E. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22160 · Cited by 44
Analyzing the Traits and Anomalies of Political Discussions on Reddit
Guimarães, A., Balalau, O., Terolli, E., & Weikum, G. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3222 · Cited by 35
Németh, R. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00196-2 · Cited by 49
“[48], measuring the amount of controversy from characteristics of the conversation graph.”
BackgroundExposure Inequality in People Recommender Systems: The Long-Term Effects
Fabbri, F., Croci, M. L., Bonchi, F., & Castillo, C. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19284 · Cited by 34
COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives
Sharma, K., Zhang, Y., & Liu, Y. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19346 · Cited by 42
Finding large balanced subgraphs in signed networks
Ordozgoiti, B., Matakos, A., & Gionis, A. (2020) · doi:10.1145/3366423.3380212 · Cited by 29
“Thus, the study of methods to detect and mitigate polarization in online debates is becoming an increasingly compelling topic [14, 15, 26, 28, 29, 33].”
BackgroundSelf-citationAn Approximate Model for Event Detection From Twitter Data
Dhiman, A., & Toshniwal, D. (2020) · IEEE Access · doi:10.1109/access.2020.3007004 · Cited by 29
“There are many real-time event detection based applications that use Twitter data such as disaster control [2], controversy tracking [3], health event tracking [4], detecting misleading events [5], detecting hate speech [6], predicting election [7], business events [8] and crime tracking [9] etc.”
BackgroundDivided We Rule: Influencer Polarization on Twitter during Political Crises in India
Dash, S., Mishra, D., Shekhawat, G., et al. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19279 · Cited by 28
Investigating the opinions distribution in the controversy on social media
Qiu, J., Lin, Z., & Shuai, Q. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.041 · Cited by 27
The impact of noise and topology on opinion dynamics in social networks
Stern, S., & Livan, G. (2021) · Royal Society Open Science · doi:10.1098/rsos.201943 · Cited by 27
“Garimella et al. [47], for example found evidence that network structure can be used to identify controversial topics, while Guerra et al. [48] similiarly showed that community boundaries, and modularity in particular, link with polarisation of viewpoints on Twitter.”
BackgroundHuang, Z., Silva, A., & Singh, A. K. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498454 · Cited by 27
A text and GNN based controversy detection method on social media
Benslimane, S., Azé, J., Bringay, S., et al. (2022) · World Wide Web · doi:10.1007/s11280-022-01116-0 · Cited by 27
“These interactions include social relation (retweet and follow on Twitter, comment on Reddit) [4, 8] and citation relation on Wikipedia [9].”
BackgroundMethodResultInfluentialA Multi-Task Model for Sentiment Aided Stance Detection of Climate Change Tweets
Upadhyaya, A., Fisichella, M., & Nejdl, W. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22194 · Cited by 31
Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter
Saeed, Z., Abbasi, R. A., Sadaf, A., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93037-4_42 · Cited by 26
Zhong, L., Cao, J., Sheng, Q., Guo, J., & Wang, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.49 · Cited by 31
Aggressive behaviour of anti-vaxxers and their toxic replies in English and Japanese
Miyazaki, K., Uchiba, T., Tanaka, K., & Sasahara, K. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01245-x · Cited by 26
“…their thoughts and claim contents (Brennen et al., 2020; Kata, 2012); behavioural patterns, emotions, topics, and positions in the social networks (Germani and Biller-Andorno, 2021); changes in their attitudes over time (Mitra et al., 2016); temporal patterns of how fake information spreads…”
Furman, I., & Tunç, A. (2019) · Policy & Internet · doi:10.1002/poi3.218 · Cited by 30
Wu, S., & Resnick, P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18105 · Cited by 34
Quantifying Polarization on Twitter: The Kavanaugh Nomination
Darwish, K. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34971-4_13 · Cited by 25
Searching for polarization in signed graphs: a local spectral approach
Xiao, H., Ordozgoiti, B., & Gionis, A. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380121 · Cited by 27
“These techniques help us locate dense structures in networks, which might correspond to so-called echo chambers [13].”
BackgroundSelf-citationViviani, M., Crocamo, C., Mazzola, M., Bartoli, F., Carrà, G., & Pasi, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.044 · Cited by 24
Sun, J., Song, J., Jiang, Y., Liu, Y., & Li, J. (2021) · Electronic Markets · doi:10.1007/s12525-021-00492-1 · Cited by 23
“In addition, other methods targeted at quantifying filter bubbles have been used, including network theory (Amrollahi, 2019; Garimella et al., 2018; Hannak et al., 2013; Thonet et al., 2017) and information diffusion (Chakraborty et al., 2017; Nikolov et al., 2019; TK et al., 2015).”
MethodMultipolar social systems: Measuring polarization beyond dichotomous contexts
Martín-Gutíerrez, S., Losada, J. C., & Benito, R. M. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113244 · Cited by 29
Controversial information spreads faster and further than non-controversial information in Reddit
Jasser, J., Garibay, I. I., Scheinert, S., & Mantzaris, A. V. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00121-z · Cited by 22
Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?
Tommasel, A., & Menczer, F. (2022) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3523227.3551473 · Cited by 23
“…, we quantify the polarization of users in terms of the Pearson correlation between users’ spreadness state and their neighbours state. iv) Random Walk Controversy Score (RWC) [10] measures polarization by computing the probability of a random user being exposed to users from the other group.”
BackgroundLenti, J., Mejova, Y., Kalimeri, K., Panisson, A., Paolotti, D., Tizzani, M., & Starnini, M. (2023) · JMIR Infodemiology · doi:10.2196/44714 · Cited by 21
“Following previous literature [10,17,52], we used the RWC score to quantify the polarization between the communities labeled as no-vax and the rest of the network.”
MethodSemEval-2020 Task 11: Detection of Propaganda Techniques in News ArticlesPreprint
Martino, G. D. S., Barrón‐Cedeño, A., Wachsmuth, H., Petrov, R., & Nakov, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.02696 · Cited by 56
“, 2019a) or has analyzed the general patterns of online propaganda (Garimella et al., 2015; Chatfield et al., 2015).”
BackgroundTyagi, A., Uyheng, J., & Carley, K. M. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00792-6 · Cited by 27
Polarization of Opinions on COVID-19 Measures: Integrating Twitter and Survey Data
Reiter-Haas, M., Klösch, B., Hadler, M., & Lex, E. (2022) · Social Science Computer Review · doi:10.1177/08944393221087662 · Cited by 23
Investigating political polarization in India through the lens of Twitter
Borah, A., & Singh, S. R. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00939-z · Cited by 21
Strategies for reducing polarization in social networks
Wu, Y., Li, L., Yu, Q., Gan, J., & Zhang, Y. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.113095 · Cited by 22
“I’m in the Bluesky Tonight”: Insights from a year worth of social data
Failla, A., & Rossetti, G. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310330 · Cited by 21
“From discourse analysis [1, 2], to studying d/misinformation [3, 4], coordinated behaviors [5, 6], radicalization [7, 8], echo-chamber effects [9, 10], and critical event detection [11, 12], computational social science has long laid its development on – and answered its question through – social…”
BackgroundEmamgholizadeh, H., Nourizade, M., Tajbakhsh, M. S., Hashminezhad, M., & Hasheminezha, F. N. E. M. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00703-1 · Cited by 24
Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures
Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512962 · Cited by 20
A Computational Analysis of Polarization on Indian and Pakistani Social Media
Tyagi, A., Field, A., Lathwal, P., Tsvetkov, Y., & Carley, K. M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60975-7_27 · Cited by 20
Cascade-based Echo Chamber Detection
Minici, M., Cinus, F., Monti, C., Bonchi, F., & Manco, G. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557253 · Cited by 21
“Thus, several studies have been devoted to providing empirical evidence of the existence of echo chambers [11, 16, 21], with variable results, depending on the specific platforms and contexts.”
BackgroundWang, H., Wang, Y., Song, X., Zhou, B., Zhao, X., & Xie, F. (2023) · World Wide Web · doi:10.1007/s11280-023-01191-x · Cited by 20
Understanding Conflicts in Online Conversations
Levy, S., Kraut, R. E., Yu, J. A., Altenburger, K. M., & Wang, Y. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512131 · Cited by 20
“These models are trained to detect conflicts at the post-level or topic-level [1, 16, 18], with the implication that all of the conversations associated with these posts and topics are either conflictful or not.”
BackgroundEvery Colour You Are: Stance Prediction and Turnaround in Controversial Issues
Graells-Garrido, E., Baeza-Yates, R., & Lalmas, M. (2020) · Web Science Conference · doi:10.1145/3394231.3397907 · Cited by 31
Should Climate Scientists Fly?
Goodwin, J. M. (2020) · Informal Logic · doi:10.22329/il.v40i2.6327 · Cited by 18
Dahlander, L., Beretta, M., Thomas, A., Kazemi, S., Fenger, M. H. J., & Frederiksen, L. (2023) · Research Policy · doi:10.1016/j.respol.2023.104875 · Cited by 17
Mangiò, F., Pedeliento, G., Andreini, D., & Zarantonello, L. (2023) · Journal of Brand Management · doi:10.1057/s41262-023-00347-4 · Cited by 19
“Used for this purpose were a computational content analysis involving both the semantic and affective aspects of CESM applying topic modeling (Roberts et al. 2014; 2019) and controversy detection analysis (Garimella et al. 2018).”
InfluentialThe Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter
Xia, Y. R., Gronow, A., Malkamäki, A., Ylä‐Anttila, T., Keller, B., & Kivelä, M. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00441-2 · Cited by 31
“Following prior work on quantifying polarization in Twitter data [14, 18], we used only the largest connected component of each network for subsequent analysis.”
MethodChavalarias, D., Bouchaud, P., & Panahi, M. (2024) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.5203 · Cited by 28
Sharevski, F., Jachim, P., & Florek, K. (2020) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3407023.3407025 · Cited by 22
“Because a Twitter user can take additional endorsement actions as a result of self-expression on Twitter [18], we utilized the following opinion expression actions one can take after reading a tweet to answer the second research question: (1) retweet it; (2) like it; (3) block the account that…”
MethodInfluentialKristensen, J. B., Henriksen, F. M., & Mayerhöffer, E. (2023) · Social Media + Society · doi:10.1177/20563051231192963 · Cited by 18
“…that is, exposure to diverse versus similar information within online information-sharing networks, has become of interest in the last decade, with the increased importance of social media for political information exposure (Balsamo et al., 2019; Bessi et al., 2015; Garimella et al., 2018).”
BackgroundRepresentativeness of Abortion Legislation Debate on Twitter: A Case Study in Argentina and Chile
Graells-Garrido, E., Baeza-Yates, R., & Lalmas, M. (2020) · The Web Conference · doi:10.1145/3366424.3383561 · Cited by 19
Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others
Banisch, S., Gaisbauer, F., & Olbrich, E. (2022) · Entropy · doi:10.3390/e24101484 · Cited by 19
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse
Hofmann, V., Schütze, H., & Pierrehumbert, J. B. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19377 · Cited by 15
Amendola, M., Cavaliere, D., Maio, C. D., et al. (2024) · Online Social Networks and Media · doi:10.1016/j.osnem.2024.100276 · Cited by 15
Privacy and uniqueness of neighborhoods in social networks
Romanini, D., Lehmann, S., & Kivelä, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-94283-5 · Cited by 15
Borrelli, D., Iandoli, L., Ramírez-Márquez, J. E., & Lipizzi, C. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3094775 · Cited by 20
Dinkelberg, A., O’Sullivan, D. J. P., Quayle, M., & MacCarron, P. (2021) · Advances in Complex Systems · doi:10.1142/s0219525921500065 · Cited by 16
Measuring Controversy in Social Networks Through NLP
de Zárate, J. M. O., Giovanni, M. D., Feuerstein, E., & Brambilla, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59212-7_14 · Cited by 17
Detection of fake images on whatsApp using socio-temporal features
Kaur, M., Daryani, P., Varshney, M., & Kaushal, R. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00883-y · Cited by 14
“Fake news spread comes under disinformation ( Kumar and Shah (2018), Fallis (2014), Garimella et al. (2018)).”
BackgroundPolitical 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
“Our approach extends previous research 25 that assumes a two-sided controversy by also handling multiple “pockets” of opinion stances, making it generalizable to multi-sided discourse around the world.”
Structure 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.”
BackgroundEscuela Superior de Ingeniería y Tecnología (ESIT). Universidad Internacional de La Rioja (UNIR). Avda. de la Paz, 1. 2. L. R. (. –. T. C. N., Carabel, T. C., García, S. A., Escuela Superior de Ingeniería y Tecnología (ESIT). Universidad Internacional de La Rioja (UNIR). Avda. de la Paz, 1. 2. L. R. (. –. S. A. N., Martínez, N. O., & Escuela Superior de Ingeniería y Tecnología (ESIT). Universidad Internacional de La Rioja (UNIR). Avda. de la Paz, 1. 2. L. R. (. –. N. O. N. (2022) · Cuadernos de Gestión · doi:10.5295/cdg.211639tc · Cited by 13
How social rewiring preferences bridge polarized communities
Borges, H. M., de Vasconcelos, V. V. L., & Pinheiro, F. L. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114594 · Cited by 17
“As such, it is not surprising that much research has been conducted to understand the phenomena of social polarization better [2–7].”
BackgroundGupta, K., Gautam, D., & Mamidi, R. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.149 · Cited by 15
Treuillier, C., Castagnos, S., Dufraisse, E., & Brun, A. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3538030 · Cited by 13
Authority without Care: Moral Values behind the Mask Mandate Response
Mejova, Y., Kalimeri, K., & Morales, G. D. F. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22173 · Cited by 13
Self-citationThe Power of Related Articles – Improving Fake News Detection on Social Media Platforms
Gimpel, H., Heger, S., Kasper, J., & Schäfer, R. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.743 · Cited by 13
Metadata
- Authors
- Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M.
- Affiliations
- Aalto University (FI)
- Qatar Computing Research Institute, Doha, Qatar
- Centre National de la Recherche Scientifique (FR)
- Venue
- ACM Transactions on Social Computing
- Published
- 2018-01-18
- Volume
- 1
- Issue
- 1
- Pages
- 1-27
- ISSN
- 2469-7818 (ISSN-L)
- 2469-7826
- OpenAlex
- W2792053130
- Semantic Scholar
- 5afbbda067ec11447bc3c330d36598137a4acc89
- Cited by
- 348
- References
- 39
- Citations by year
- 2026: 22 · 2025: 39 · 2024: 44 · 2023: 52 · 2022: 51 · 2021: 52 · 2020: 48 · 2019: 29 · 2018: 10
- FWCI
- 19.65
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Transactions on Social Computing (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Funders
- European Commission (654024)
- Academy of Finland (286211)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- controversy detection, social media, Twitter, graph partitioning, content features, news dissemination
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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