VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Abilov, Anton; Hua, Yiqing; Matatov, Hana; Amir, Ofra; Нааман, Мор

doi:10.48550/arxiv.2101.08210

Cited by 11 publications

(28 citation statements)

References 20 publications

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“…To answer our research questions, we combine two publicly-available datasets: the VoterFraud2020 dataset [1] and the MeLa-BitChute dataset [30].…”

Section: Datamentioning

confidence: 99%

“…The VoterFraud2020 dataset contains 7.6M tweets and 25.6M retweets related to election fraud claims during the 2020 U.S. Presidential Election [1]. The data was collected using the Twitter streaming API over a manually curated set of hashtags and keywords related to election fraud claims.…”

Section: The Voterfraud2020 Datasetmentioning

confidence: 99%

“…The data was collected between October 23rd, 2020 and December 16th, 2020. Based on Abilov et al [1]'s analysis, the dataset covers roughly 60% of the content on Twitter that used the tracked keywords.…”

Section: The Voterfraud2020 Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing YouTube and BitChute Content and Mobilizers During U.S. Election Fraud Discussions on Twitter

Childs¹,

Buntain²,

Trujillo³

et al. 2022

Preprint

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In this study, we characterize the cross-platform mobilization of YouTube and BitChute videos on Twitter during the 2020 U.S. Election fraud discussions. Specifically, we extend the VoterFraud2020 dataset [1] to describe the prevalence of content supplied by both platforms, the mobilizers of that content, the suppliers of that content, and the content itself. We find that while BitChute videos promoting election fraud claims were linked to and engaged with in the Twitter discussion, they played a relatively small role compared to YouTube videos promoting fraud claims. This core finding points to the continued need for proactive, consistent, and collaborative content moderation solutions rather than the reactive and inconsistent solutions currently being used. Additionally, we find that cross-platform disinformation spread from video platforms was not prominently from bot accounts or political elites, but rather average Twitter users. This finding supports past work arguing that research on disinformation should move beyond a focus on bots and trolls to a focus on participatory disinformation spread.

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“…To answer our research questions, we combine two publicly-available datasets: the VoterFraud2020 dataset [1] and the MeLa-BitChute dataset [30].…”

Section: Datamentioning

confidence: 99%

Section: The Voterfraud2020 Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing YouTube and BitChute Content and Mobilizers During U.S. Election Fraud Discussions on Twitter

Childs¹,

Buntain²,

Trujillo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, Naíve Realism [22] indicates that consumers tend to believe that their perceptions of reality are the only accurate views, while others who disagree are regarded as uninformed, irrational, or biased; and Confirmation Bias theory [18] reveals that consumers prefer to receive information that confirms their existing views. For instance, a user believes the election fraud would probably share similar news with a supportive stance, and the news asserting election is stolen would attract users with similar beliefs [1]. To model user endogenous preferences, existing works have attempted to utilize historical posts as a proxy and have shown promising performance to detect sarcasm [13], hate speech [20], and fake news spreaders [21] on social media.…”

mentioning

confidence: 99%

“…We propose an end-to-end fake news detection framework named User Preference-aware Fake Detection (UPFD) to model endogenous preference and exogenous context jointly (as shown in Figure 1). Specifically, UPFD consists of the following major components: (1) To model the user endogenous preference, we encode news content and user historical posts using various text representation learning approaches. (2) To obtain the user exogenous context, we build a tree-structured propagation graph for each news based on its sharing cascading on social media.…”

mentioning

confidence: 99%

User Preference-aware Fake News Detection

Dou

Shu

Xia

et al. 2021

Preprint

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Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored. The confirmation bias theory has indicated that a user is more likely to spread a piece of fake news when it confirms his/her existing beliefs/preferences. Users' historical, social engagements such as posts provide rich information about users' preferences toward news and have great potentials to advance fake news detection. However, the work on exploring user preference for fake news detection is somewhat limited. Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. We release our code and data as a benchmark for GNN-based fake news detection: https://github.com/safe-graph/GNN-FakeNews.

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Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election

Youngblood,

Stubbersfield,

Morin

et al. 2023

Humanit Soc Sci Commun

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During the 2020 US presidential election, conspiracy theories about large-scale voter fraud were widely circulated on social media platforms. Given their scale, persistence, and impact, it is critically important to understand the mechanisms that caused these theories to spread. The aim of this preregistered study was to investigate whether retweet frequencies among proponents of voter fraud conspiracy theories on Twitter during the 2020 US election are consistent with frequency bias and/or content bias. To do this, we conducted generative inference using an agent-based model of cultural transmission on Twitter and the VoterFraud2020 dataset. The results show that the observed retweet distribution is consistent with a strong content bias causing users to preferentially retweet tweets with negative emotional valence. Frequency information appears to be largely irrelevant to future retweet count. Follower count strongly predicts retweet count in a simpler linear model but does not appear to drive the overall retweet distribution after temporal dynamics are accounted for. Future studies could apply our methodology in a comparative framework to assess whether content bias for emotional valence in conspiracy theory messages differs from other forms of information on social media.

show abstract

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Cited by 11 publications

References 20 publications

Characterizing YouTube and BitChute Content and Mobilizers During U.S. Election Fraud Discussions on Twitter

Characterizing YouTube and BitChute Content and Mobilizers During U.S. Election Fraud Discussions on Twitter

User Preference-aware Fake News Detection

Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election

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