The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Anatomy of an online misinformation network
Open accessAbstract
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
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The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407
Social bots distort the 2016 U.S. Presidential election online discussion
Bessi, A., & Ferrara, E. (2016) · First Monday · doi:10.5210/fm.v21i11.7090 · Cited by 993
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 981 · cited 2×
“nalyze the accounts in the core of the network to identify those users who play an important role in the diusion of misinformation. The use of Botometer, a state-of-the-art social bot detection tool [12], reveals a higher presence of social bots in the main core. We also consider a host of centrality measures (in-stre…”
BackgroundSelf-citationBotOrNot: A System to Evaluate Social Bots
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Proceedings of the 25th International Conference Companion on World Wide Web - WWW 16 Companion · doi:10.1145/2872518.2889302 · Cited by 909
Self-citationRatkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Disinformation and social bot operations in the run up to the 2017 French presidential election
Ferrara, E. (2017) · First Monday · doi:10.5210/fm.v22i8.8005 · Cited by 524
Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes
Kumar, S., West, R., & Leskovec, J. (2016) · Proceedings of the 25th International Conference on World Wide Web · doi:10.1145/2872427.2883085 · Cited by 310
Anatomy of news consumption on Facebook
Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617052114 · Cited by 305
Partisan asymmetries in online political activity
Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012) · EPJ Data Science · doi:10.1140/epjds6 · Cited by 234
Self-citationHow algorithmic popularity bias hinders or promotes quality
Ciampaglia, G. L., Nematzadeh, A., Menczer, F., & Flammini, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-34203-2 · Cited by 204
Self-citation
Cited by
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112552 · Cited by 1,806
“Other relevant studies have focused on important concepts such as misperception and confirmation bias, whereby people's views on factual matters are strongly influenced by prior beliefs (Taber and Lodge, 2006; Nyhan and Reifler, 2010; Jerit and Barabas, 2012) ; polarization within networks (Lewandowsky et al., 2012) ;…”
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125
Self-citationFake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772
Influence of fake news in Twitter during the 2016 US presidential election
Bovet, A., & Makse, H. A. (2019) · Nature Communications · doi:10.1038/s41467-018-07761-2 · Cited by 701
Evaluating the fake news problem at the scale of the information ecosystem
Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020) · Science Advances · doi:10.1126/sciadv.aay3539 · Cited by 528
Metadata
- Authors
- Shao, C., Hui, P.-M., Wang, L., Jiang, X., Flammini, A., Menczer, F., & Ciampaglia, G. L.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-04-27
- Volume
- 13
- Issue
- 4
- Pages
- e0196087
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2784117065
- Cited by
- 278
- References
- 11
- FWCI
- 82.01
- Type
- journal article
- Language
- en
- Open access
- gold
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