Propagation From Deceptive News Sources Who Shares, How Much, How Evenly, and How Quickly?

As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media, we analyze 11 million posts and investigate the propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus subpopulation behavior (by demographics) when spreading misinformation, and distinguish between the two types of propagation, i.e., direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.

[1]  Edson C. Tandoc,et al.  Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines , 2015, Comput. Hum. Behav..

[2]  Emilio Ferrara,et al.  Contagion dynamics of extremist propaganda in social networks , 2017, Inf. Sci..

[3]  Ted Goertzel,et al.  Belief in Conspiracy Theories , 1994 .

[4]  Wanlei Zhou,et al.  A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks , 2015, IEEE Transactions on Computers.

[5]  Naren Ramakrishnan,et al.  Epidemiological modeling of news and rumors on Twitter , 2013, SNAKDD '13.

[6]  Huan Liu,et al.  Mining Misinformation in Social Media , 2016 .

[7]  G. Geethakumari,et al.  Detecting misinformation in online social networks using cognitive psychology , 2014, Human-centric Computing and Information Sciences.

[8]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[9]  Venkata Rama Kiran Garimella,et al.  Political hashtag hijacking in the U.S. , 2013, WWW.

[10]  Stephan Lewandowsky,et al.  NASA Faked the Moon Landing—Therefore, (Climate) Science Is a Hoax , 2013, Psychological science.

[11]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[12]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[13]  Derek Ruths,et al.  Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter , 2015, ICWSM.

[14]  T. Schelling Micromotives and Macrobehavior , 1978 .

[15]  Svitlana Volkova,et al.  Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast , 2016, ACL.

[16]  Kenny Q. Zhu,et al.  False rumors detection on Sina Weibo by propagation structures , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[17]  Filippo Menczer,et al.  The spread of fake news by social bots , 2017, ArXiv.

[18]  Svitlana Volkova,et al.  On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure , 2015, Cyberpsychology Behav. Soc. Netw..

[19]  E. Hargittai,et al.  THE PARTICIPATION DIVIDE: Content creation and sharing in the digital age1 , 2008 .

[20]  Wei Gao,et al.  From Retweet to Believability: Utilizing Trust to Identify Rumor Spreaders on Twitter , 2017, ASONAM.

[21]  N. Kakwani,et al.  On the Estimation of Lorenz Curves from Grouped Observations , 1973 .

[22]  Trevor van Mierlo The 1% Rule in Four Digital Health Social Networks: An Observational Study , 2014, Journal of medical Internet research.

[23]  Jeffrey A. Gottfried,et al.  News use across social media platforms 2016 , 2016 .

[24]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[25]  Emilio Ferrara,et al.  Quantifying the Effect of Sentiment on Information Diffusion in Social Media , 2015, PeerJ Comput. Sci..

[26]  Tim Weninger,et al.  Rating Effects on Social News Posts and Comments , 2016, ACM Trans. Intell. Syst. Technol..

[27]  Filippo Menczer,et al.  Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks , 2015, WWW.

[28]  Emilio Ferrara,et al.  Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election , 2017, First Monday.

[29]  Svitlana Volkova,et al.  Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter , 2017, ACL.