Rumor Cascades
By Adrien Friggeri, Lada A. Adamic, Dean Eckles, Justin Cheng. International Conference on Web and Social Media, 2014
489 citations recorded by the source corpus.
Online social networks provide a rich substrate for rumor propagation. Information received via friends tends to be trusted, and online social networks allow individuals to transmit information to many friends at once. By referencing known rumors from Snopes.com, a popular website documenting memes and urban legends, we track the propagation of thousands of rumors appearing on Facebook. From this sample we infer the rates at which rumors from different categories and of varying truth value are uploaded and reshared. We find that rumor cascades run deeper in the social network than reshare cascades in general. We then examine the effect of individual reshares receiving a comment containing a link to a Snopes article on the evolution of the cascade. We find that receiving such a comment increases the likelihood that a reshare of a rumor will be deleted. Furthermore, large cascades are able to accumulate hundreds of Snopes comments while continuing to propagate. Finally, using a dataset of rumors copied and pasted from one status update to another, we show that rumors change over time and that different variants tend to dominate different bursts in popularity.
Subfields: Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/1e3d1055fc65966bc2c64505785207d7e8d71022
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Citations in the corpus
- Spread of Hate Speech in Online Social Media (2018, background): “However, there are several works that looks into diffusion of fake news [26, 37, 41], Linkedin [3], retweet cascade [8, 9, 18, 37], rumours [11, 17, 20, 22, 45] and Tumblr [1, 2, 7, 42].”
- Political Fact-Checking on Twitter: When Do Corrections Have an Effect? (2018, methodology): “Our study departs from recent work on rumors and fact-checking by examining the behavior of the corrected person as opposed to those in the social network as a whole (e.g., Friggeri et al., 2014; Shin et al., 2016).”
- CSI: A Hybrid Deep Model for Fake News Detection (2017, background): “From a slightly dierent point of view, one popular approach has been to measure the response an article received by studying its propagation on a social graph [12, 16, 17, 35].”
- Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, background): “Friggeri et al. (2014) and Hannak et al. (2014) studied the structure of misinformation cascades by analyzing comments linking to rumor debunking websites.”
- Hoaxy: A Platform for Tracking Online Misinformation (2016, background): “The worrying consequences in the offline world [8, 22, 29, 23, 27, 7]; examples include rumors [18], false news [8, 23], hoaxes, and Hurricane Sandy, false reports that the New York Stocks Exchange had been flooded were corrected in less than an hour [36].”
- Combating Fake News (2019, background): “False information can easily reach and impact a large number of users in short time [31, 89].”
- Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, background): “Both Kwon et al. [17] and Friggeri et al. [13] tracked the judgments made by rumor debunking websites such as Snopes.com.”
- The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016 (2018, result): “This is consistent with other research indicating that corrections do not spread as widely as misinformation (Friggeri et al., 2014; Zollo et al., 2015).”
- Novel Visual and Statistical Image Features for Microblogs News Verification (2017, background): “Without verifying the truthfulness of news, fake news would spread promptly through social network and result in serious consequences [1].”
- Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “For example, scholars have studied the propagation and correction of rumors(Friggeri et al. 2014), the role of in-fluential users in spreading information (Bakshy et al. 2012), or whether information actually diffuses in a viral way at all (Goel, Watts, and Goldstein 2012).”
- Misinformation in Social Media: Definition, Manipulation, and Detection (2019, result): “Similar observations have been found in an earlier study that rumors are periodically popular in bursts [9].”
- Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster (2017, background): “Rumor detection aims at finding rumors in social media, by considering linguistic signals in the content of social media posts, signature text phrases in users’ comments that express skepticism, how they were spread, as well as the credibility of the authors based on track record [3, 7, 19].”
- Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online (2018, background): “Quite often the distribution of fake images will far surpass the distribution of any correction or attempt to expose the forgery (Friggeri et al., 2014).”
- Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes (2016, background): “, in Facebook [9, 15], Twitter [16], and Sina Weibo [42].”
- False Information on Web and Social Media: A Survey (2018, background): “[30] concluded that false information reshare cascades spread much deeper compared to that of true reference cascades.”