The theater of fake news spreading, who plays which role? A study on real graphs of spreading on Twitter

Bodaghi, Amirhosein; Oliveira, Jonice

doi:10.1016/j.eswa.2021.116110

Cited by 49 publications

(18 citation statements)

References 54 publications

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“…For example, as food poisoning in popular food franchises can harm a person's health, people are more likely to share such news with others (Lee et al, 2021). Indeed, fake news spreads to exponentially more people within a user cluster compared to true news (Abilov et al, 2021;Bodaghi & Oliveira, 2022;Bovet & Makse, 2019). Hence, we propose hypothesis H1: H1: A tweet linked to a fake news story (rather than a true one) ignites more user cluster tweets on this topic (total users).…”

Section: Information Behaviorsmentioning

confidence: 97%

Election Fraud and Misinformation on Twitter: Author, Cluster, and Message Antecedents

Chiu

Park

Lee

et al. 2022

MaC

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This study determined the antecedents of diffusion scope (total audience), speed (number of adopters/time), and shape (broadcast vs. person-to-person transmission) for true vs. fake news about a falsely claimed stolen 2020 US Presidential election across clusters of users that responded to one another’s tweets (“user clusters”). We examined 31,128 tweets with links to fake vs. true news by 20,179 users to identify 1,069 user clusters via clustering analysis. We tested whether attributes of authors (experience, followers, following, total tweets), time (date), or tweets (link to fake [vs. true] news, retweets) affected diffusion scope, speed, or shape, across user clusters via multilevel diffusion analysis. These tweets showed no overall diffusion pattern; instead, specific explanatory variables determined their scope, speed, and shape. Compared to true news tweets, fake news tweets started earlier and showed greater broadcast influence (greater diffusion speed), scope, and person-to-person influence. Authors with more experience and smaller user clusters both showed greater speed but less scope and less person-to-person influence. Likewise, later tweets showed slightly more broadcast influence, less scope, and more person-to-person influence. By contrast, users with more followers showed less broadcast influence but greater scope and slightly more person-to-person influence. These results highlight the earlier instances of fake news and the greater diffusion speed of fake news in smaller user clusters and by users with fewer followers, so they suggest that monitors can detect fake news earlier by focusing on earlier tweets, smaller user clusters, and users with fewer followers.

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Section: Information Behaviorsmentioning

confidence: 97%

Election Fraud and Misinformation on Twitter: Author, Cluster, and Message Antecedents

Chiu

Park

Lee

et al. 2022

MaC

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“…definition, the very nature of rumors is uncertainty that means rumors come along with anti-rumors. The confrontations of these two on social media impact their spreading through the network [4]. For instance, in the case of the Zika Virus Outbreak, the researchers [5] analyzed rumor and anti-rumor and noticed the differences between their diffusion networks which has a direct impact on the spreading process in general.…”

Section: Competitive Nature Of Rumors On Social Mediamentioning

confidence: 99%

The Rumor Categorizer: An open-source software for analyzing rumor posts on Twitter

2022

Self Cite

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The Rumor Categorizer Software (RCS), an open-source software, categorizes Twitter posts based on their stance towards a given rumor into six categories; rumor tweet, anti-rumor tweet, rumor retweet, anti-rumor retweet, rumor-related tweet, and rumor-related retweet. Then for the publishers of those tweets/retweets, analyze the user dynamics. Furthermore, the software calculates the number of users belonging to any combination of the sextuple categories and plots the number of tweets/retweets in each hour for rumor and anti-rumor. The software aids researchers and practitioners in analyzing rumors effectively.

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Section: Information Behaviorsmentioning

confidence: 99%

Editorial: Networks and Organizing Processes in Online Social Media

Lee¹

2022

MaC

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Online social media present unprecedented opportunities and challenges for a range of organizing processes such as information sharing, knowledge creation, collective action, and post-disaster resource mobilization. Concepts and tools of network research can help highlight key aspects of online interaction. This editorial introduction frames the thematic issue along three themes of networked processes: identity and identification; interaction patterns in online communities; and challenges and cautionary notes concerning social media organizing. A diverse range of country contexts, as well as theoretical and methodological approaches illustrated in this issue, represent the multifaceted research that scholars can undertake to understand networked organizing on social media.

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The theater of fake news spreading, who plays which role? A study on real graphs of spreading on Twitter

Cited by 49 publications

References 54 publications

Election Fraud and Misinformation on Twitter: Author, Cluster, and Message Antecedents

Election Fraud and Misinformation on Twitter: Author, Cluster, and Message Antecedents

The Rumor Categorizer: An open-source software for analyzing rumor posts on Twitter

Editorial: Networks and Organizing Processes in Online Social Media

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