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”
Citations
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Items 201–300 of 347
Counteracting Filter Bubbles with Homophily-Aware Link Recommendations
Warton, R., Volny, C., & Xu, K. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17114-7_15 · Cited by 3
Polarizing Opinion Dynamics with Confirmation Bias
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PyPoll: A python library automating mining of networks, discussions and polarization on Twitter
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“Opinion attribution: In the polarization analysis we consider (and similarly to several previous works [11, 15]), users are assigned to an "opinion" (or, "group" or community) based on the account(s) they follow.”
BackgroundMethodInfluentialSocial Media Opinion Analysis Model Based on Fusion of Text and Structural Features
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Progress Tracking and Responding to Online Public Shaming Events on Twitter
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An organizational form framework to measure and interpret online polarization
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“Scholars using observational user-generated content to examine the dynamics of polarized communities online typically de fi ne echo chamber-like groups as the most polarized (Conover et al., 2011; Garimella et al., 2018b; Guerra et al., 2013).”
BackgroundMethodInfluentialInfluence of Opinion Formation on Polarization in Social Networks
Das, S., Dutta, S. M., Chakraborty, S., & Biswas, S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307862 · Cited by 3
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Learning Unbiased News Article Representations: A Knowledge-Infused Approach
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Policy influence and influencers online and off
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A SHAP-based controversy analysis through communities on Twitter
Benslimane, S., Παπαστεργίου, Θ., Azé, J., Bringay, S., Servajean, M., & Mollévi, C. (2024) · World Wide Web · doi:10.1007/s11280-024-01278-z · Cited by 4
Decoding Political Polarization in Social Media Interactions
Pecile, G., Marco, N. D., Cinelli, M., & Quattrociocchi, W. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82435-7_23 · Cited by 4
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“Garimella et al. [15] proposed a graph-based approach to identify controversial topics on Twitter and measures to quantify controversy of a topic.”
BackgroundSpheres of legislation: polarization and most influential nodes in behavioral context
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“Garimella et al. [29] proposed the use of a graph-based measure by measuring the level of separation of communities within the graph.”
Spotting Flares: The Vital Signs of the Viral Spread of Tweets Made During Communal Incidents
Upadhyaya, A., & Chandra, J. (2022) · ACM Transactions on the Web · doi:10.1145/3550357 · Cited by 3
Opinion Discovery Framework: Toward a Quality Opinion-Centric Platform
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Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?
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Can Celebrities Burst Your Bubble?Preprint
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“Besides, the controversy of the vaccine debate computed using the Random Walk process described by Garimella et al. [12] is high ( ≈ 0 .”
InfluentialHafid, S., Schellhammer, S., Kartal, Y. S., Παπαστεργίου, Θ., Dietze, S., Bringay, S., & Todorov, K. (2025) · ACM Transactions on the Web · doi:10.1145/3746170 · Cited by 2
The price of controversy: CEO Twitter activity and stock market performance
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Kolic, B., Aguirre-López, F., Hernández-Williams, S., & Garduño-Hernández, G. (2025) · Journal of Complex Networks · doi:10.1093/comnet/cnaf020 · Cited by 2
“Garimella et al. [32] state that opinions or beliefs stay inside communities created by like-minded people who reinforce and endorse each other’s opinions.”
InfluentialArathi, E., & Sasikala, S. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-7524-0_26 · Cited by 2
DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection
Hua, C., Yang, W., Wang, L., Wei, F., Kezierbieke, H., & Liao, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040862 · Cited by 2
Peiris, P. A. S., Herath, T. C., Dissanayaka, R., et al. (2023) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac59571.2023.10368503 · Cited by 2
Cite-worthiness Detection on Social Media: A Preliminary Study
Hafid, S., Ammar, W., Bringay, S., & Todorov, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65794-8_2 · Cited by 3
Impiccichè, P., & Viviani, M. (2024) · ACM Transactions on the Web · doi:10.1145/3707701 · Cited by 2
“…proposed by Morales et al. [34], Random Walk Controversy (RWC), Betweennees Centrality Controversy (BCC), and Embedding Controversy (EC), proposed by Garimella et al. [19], Displacement Random Walk Controversy (DRWC) and Authorative Random Walk Controversy (ARWC), proposed by Villa et al. [45].”
InfluentialCho, S., Hisamitsu, S., Jin, H., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299935 · Cited by 2
Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity
Preti, G., Riondato, M., Gionis, A., & Morales, G. D. F. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703560 · Cited by 5
Self-citationWith Visual Integrity and Care: A Framework for Mixed Methods Research on Visual Social Data
Lutz, N., Schafer, J. S., Dhawka, P., Tinn, P., & Starbird, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791623 · Cited by 2
“Most methodological innovations in social computing focus on textual data, offering new measures (e.g., metrics, signals) or research instruments (e.g., interactive graphs, dashboards, coding schemas) [36, 59, 101, 158, 182].”
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support
Skitalinskaya, G., & Wachsmuth, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.880 · Cited by 4
Análise de Sentimentos em Tweets em Português Brasileiro
Kansaon, D. P., Brandão, M. A., & Pinto, S. A. D. P. (2018) · doi:10.5753/brasnam.2018.3578 · Cited by 2
Collaboration of Open Content News in Wikipedia: The Role and Impact of Gatekeepers
Li, A., & Farzan, R. (2020) · The Web Conference (WWW) · doi:10.1145/3366424.3383568 · Cited by 2
Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model
Singh, M., Iyengar, S. R. S., & Kaur, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95405-5_16 · Cited by 4
ERIS: An Approach Based on Community Boundaries to Assess Polarization in Online Social Networks
Guyot, A., Gillet, A., Leclercq, É., & Cullot, N. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-05760-1_6 · Cited by 2
Extremism on Social Media: Lynching of Priyantha Kumara Diyawadana
Musa, M., Usama, M., & Uppal, M. (2022) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam55673.2022.10068622 · Cited by 2
Diachronic Analysis of Users' Stances on COVID-19 Vaccination in Japan using Twitter
Hisamitsu, S., Cho, S., Jin, H., Toyoda, M., & Yoshinaga, N. (2022) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam55673.2022.10068695 · Cited by 2
“To quantify the polarization, we used the Random Walk Controversy (RWC) [17] for measuring the degree of separation between two communities in a graph as the ratio of random walks staying in the same community.”
MethodBridging or Breaking: Impact of Intergroup Interactions on Religious Polarization
Chaturvedi, R., Chaturvedi, S., & Zheleva, E. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645675 · Cited by 2
“Garimella et al. [25] use graph clustering and network-based metrics to identify polarizing topics.”
BackgroundWallStreetBets: Assessing the Collective Intelligence of Reddit for Investment Advice
Buz, T., & de Melo, G. (2024) · ACM Transactions on Social Computing · doi:10.1145/3660760 · Cited by 4
“Similarly, one can assess the spread of false information [9] and quantify controversy in discussions [15].”
BackgroundIdentifying Distributional Perspectives from Colingual Groups
Tian, Y., Chakrabarty, T., Morstatter, F., & Peng, N. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.16 · Cited by 5
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they DevelopPreprint
Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01362 · Cited by 14
Sharevski, F., Jachim, P., & Florek, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12093 · Cited by 4
“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…”
MethodInfluentialDetect opinion-based groups and reveal polarisation in survey dataPreprint
Dinkelberg, A., O’Sullivan, D. J. P., Quayle, M., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14427 · Cited by 2
Peran Profetik Media Sosial dalam Mengadvokasi Keadilan Sosial
Triono, T. A., & Sangaji, R. C. (2023) · Journal of Society Bridge · doi:10.59012/jsb.v1i2.8 · Cited by 1
Madraki, G., Otala, J. M., Vahidpour, B., Lukaszewski, M., Compani, M., & Matthews, J. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2360508 · Cited by 2
“…these studies, usually methods based on both content and network structure have been used such as homophily and segregation measures, random walks (Garimella et al., 2018; Jiang et al., 2021), balance theory (Interian et al., 2023), community detection (Blondel et al., 2008), and antagonism…”
An Analysis of Relationship Between Social Bots and Echo Chambers on X in Modern Japan
Ippa, S., Okubo, T., & Hashimoto, M. (2024) · International Symposium on Computing and Networking · doi:10.1109/candar64496.2024.00014 · Cited by 2
“Note that when the degree between vertices is one, there is concern that the interaction between people with similar opinions are insufficient and results in noise [58].”
Analysis of Surprisingly Popular Voting for Opinion Aggregation on Social Networks
Yamashita, Y., Sakurai, Y., Oyama, S., Onishi, M., & Morishima, A. (2025) · IEEE Access · doi:10.1109/access.2025.3532754 · Cited by 1
Dynamical evolution of social network polarization and its impact on the propagation of a virus
Achitouv, I., & Chavalarias, D. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116676 · Cited by 1
Cheng, X., & Jin, J. (2025) · Entropy · doi:10.3390/e27070699 · Cited by 1
“However, some recent studies have challenged this conclusion [68].”
Bassolas, A., Massachs, J., Cozzo, E., & Vicens, J. (2025) · Royal Society Open Science · doi:10.1098/rsos.241974 · Cited by 6
Media bias and polarization through the lens of a Markov switching latent space network model
Casarin, R., Peruzzi, A., & Steel, M. F. J. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2069 · Cited by 1
Don't You Know That You're Toxic? How Influencer‐Driven Misinformation Fuels Online Toxicity
Domenico, G. D., Mangiò, F., & Dineva, D. (2026) · Psychology and Marketing · doi:10.1002/mar.70106 · Cited by 1
Opinion dynamics: Statistical physics and beyond
Starnini, M., Baumann, F., Galla, T., Garcia, D., Íñiguez, G., Karsai, M., Lorenz, J., & Sznajd-Weron, K. (2026) · Reviews of Modern Physics · doi:10.1103/j1zg-ddqv · Cited by 51
Wang, H., Song, X., Zhou, B., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90888-1_30 · Cited by 1
Partitioning-Aware Performance Modeling of Distributed Graph Processing Tasks
Presser, D., & Siqueira, F. (2023) · International Journal of Parallel Programming · doi:10.1007/s10766-023-00753-w · Cited by 1
“In this context, graph processing can unveil applications such as social trend analysis [5, 9, 33], advertising [6], fraud detection [29], and several other data analytics applications, which are valuable for business management and for scientific research.”
BackgroundExplaining controversy through community analysis on Twitter
Benslimane, S., Παπαστεργίου, Θ., Azé, J., et al. (2023) · doi:10.1145/3589462.3589480 · Cited by 1
Buturoiu, R., Corbu, N., & Boţan, M. (2023) · Springer studies in media and political communication · doi:10.1007/978-3-031-41954-6_6 · Cited by 1
Tommasel, A., & Assent, I. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386616 · Cited by 1
“…metric that does not only assess how users are connected but also how actively they could communicate and influence others [44]; iii) Random Walk Controversy Score (RWC) [45], which is based on PageRank and computes the probability of a random user being exposed to users from the other group.”
BackgroundEvolution of impact of Social media using the COPRAS Method
Bhardwaj, D., & Kumar, M. (2023) · doi:10.1109/icccee55951.2023.10424463 · Cited by 1
Anatomy of elite and mass polarization in social networks
Salloum, A., Chen, T. H. Y., & Kivelä, M. (2025) · Network Science · doi:10.1017/nws.2025.10010 · Cited by 10
“While these methods have been successfully applied to report polarization trends online [22], they fall short of capturing the full picture.”
InfluentialPositive Communities on Signed Graphs That Are Not Echo Chambers: A Clique-Based Approach
Zhou, A., Wang, Y., Chen, L., & ÖZSU, M. T. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00199 · Cited by 1
“Some of these methods such as Homophily [14] and Random Walk Contro-versy [18] require knowledge of which user belongs to which group (often defaulting to an issue with only two viewpoints).”
MethodStrategic behaviour in the mean field Ising game
Leonidov, A. V., Radionov, S. A., & Vasilyeva, E. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.115416 · Cited by 1
Wiser than the Wisest of Crowds: The Asch Effect and Polarization Revisited
Ristache, D., Spaeh, F., & Tsourakakis, C. E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70362-1_26 · Cited by 2
Análise de Algoritmos de Classificação para Detecção de Emoções em Tweets em Português Brasileiro
Kansaon, D. P., Brandão, M. A., & Pinto, S. A. D. P. (2019) · iSys - Brazilian Journal of Information Systems · doi:10.5753/isys.2019.600 · Cited by 1
Violence Identification in Social Media
Vizcarra, J., Fukuda, K., & Kozaki, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41407-8_3 · Cited by 1
Using Large Pre-Trained Language Models to Track Emotions of Cancer Patients on Twitter
Baker, W. (2021) · Journal of the Arkansas Academy of Science · Cited by 1
Identifying Corporate Political Trends Online
Maunder, L., Lyons, J., Anderson, L. R., et al. (2021) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi53945.2021.9624686 · Cited by 1
Quantifying Polarization on Twitter: the Kavanaugh NominationPreprint
Darwish, K. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02125 · Cited by 1
An Integrated Approach for Identifying Topical Experts in MicroblogsPreprint
Agness, J. A., & Raj, J. R. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3170290 · Cited by 1
POLE: Polarized Embedding for Signed NetworksPreprint
Huang, Z., Silva, A., & Singh, A. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09899 · Cited by 1
On the Complexity of Opinions and Online Discussions
Upadhyay, U., De, A., Pappu, A., et al. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290965
“For example, our measure of complexity—the dimension of the latent space of opinions—may be a good starting point to develop theoretically grounded measures of polarization [10, 11, 27] and controversy [15, 16], which have been lacking in the literature.”
BackgroundUnderstanding Mass Media Using Facebook Like Activities
Wang, M.-H. (2019) · Conference for Truth and Trust Online (TTO) · doi:10.36370/tto.2019.24
Characterizing Behavioral Trends in a Community Driven Discussion Platform
Thukral, S., Chatterjee, A., Meisheri, H., Kataria, T., Agarwal, A., Verma, I., & Dey, L. (2019) · Lecture notes in social networks · doi:10.1007/978-3-030-33698-1_8
Zhong, L., Cao, J., Sheng, Q., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07886
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums.Preprint
Sokolova, M. (2020) · arXiv (Cornell University)
On the Complexity of Opinions and Online Discussions
Upadhyay, U., De, A., Pappu, A., et al. (2019) · MPG.PuRe (Max Planck Society)
Polarization Model of Online Social Networks Based on the Concept of Spontaneous Symmetry BreakingPreprint
Aida, M., Hashizume, A., Takano, C., & Murata, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05393
Separating Controversy from Noise: Comparison and Normalization of Structural Polarization MeasuresPreprint
Salloum, A., Chen, T. H. Y., & Kivelä, M. (2021) · arXiv (Cornell University)
Auditing Wikipedia's Hyperlinks Network on Polarizing TopicsPreprint
Menghini, C., Anagnostopoulos, A., & Upfal, E. (2020) · arXiv (Cornell University)
Identifying Distributional Perspective Differences from Colingual GroupsPreprint
Tian, Y., Chakrabarty, T., Morstatter, F., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04938
Retrospective Analysis of Controversial Subtopics on COVID-19 in JapanPreprint
Miyazaki, K., Uchiba, T., Toriumi, F., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09354
How Inclusive Are Wikipedia’s Hyperlinks in Articles Covering Polarizing Topics?
Menghini, C., Anagnostopoulos, A., & Upfal, E. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671943
CONTENT ANALYSIS OF THE WRITTEN WORKS BY @SENYORA SANTIBAÑEZ IMPLICATION IN LANGUAGE TEACHING
Noval, L. M. M., & Limpot, M. Y. (2022) · EPRA International Journal of Multidisciplinary Research (IJMR) · doi:10.36713/epra9906
Bertaglia, T., Dubois, A., & Goanță, C. (2021) · Morals & Machines · doi:10.5771/2747-5174-2021-1-24
Bertaglia, T., Dubois, A., & Goanță, C. (2021) · Morals & Machines · doi:10.5771/2747-5182-2021-1-24
BeyondFacts’22: 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis
Todorov, K., Fafalios, P., & Dietze, S. (2022) · The Web Conference (WWW) · doi:10.1145/3487553.3524863
“…claims on Twitter [16], or they aim at computational methods, like pipelines for detecting the stance of claim-relevant Web documents [17], understanding/quantifying hidden biases [11], classifying sources of news [13], or research into fake news detection [14], and automatic fact-checking [2].”
CNN-BASED FEATURES FOR FILTERING OF CRISIS RELATED SOCIAL MEDIA MESSAGES
Anh, Đ. N., Anh, N. Q., & Hùng, L. M. (2022) · TNU Journal of Science and Technology · doi:10.34238/tnu-jst.6133
Durrheim, K. L., & Schuld, M. (2023) · doi:10.31234/osf.io/n47xe
Identifying Behavioral Factors Leading to Differential Polarization Effects of Adversarial Botnets
(2023) · ACM SIGAPP Applied Computing Review · doi:10.1145/3610019.3610022
“On this camp and botnet labeled network, (5) we utilize the Random Walk Controversy Score (RWCS) [22] and design experiments to measure the polarization effects of adversarial botnets.”
BackgroundMethodInfluentialYang, C. (2023) · SN Applied Sciences · doi:10.1007/s42452-023-05514-5
Approximation Methods for Influence Maximization in Temporal Networks
Murata, T., & Koga, H. (2023) · Computational social sciences · doi:10.1007/978-3-031-30399-9_18
Alemayehu, E., & Fang, Y. (2023) · Intelligent Data Analysis · doi:10.3233/ida-227202 · Cited by 1
“Some recent work addresses the task of identifying controversial contents on Wikipedia [63,64,65] and on social media [66, 67,68].”
Seitz, P., Eisenegger, M., & Bergman, M. M. (2023) · MDPI eBooks · doi:10.3390/books978-3-0365-1347-8
Guyot, A., Gillet, A., Leclercq, É., & Cullot, N. (2024) · Revue ouverte d’ingénierie des systèmes d’information · doi:10.21494/iste.op.2024.1137
Food culture and food sustainability on social media
Mejova, Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91227-3.00010-x
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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