Truthy: mapping the spread of astroturf in microblog streams

By Jacob Ratkiewicz, Michael D. Conover, Mark R. Meiss, Bruno Gonçalves, Snehal Patil, Alessandro Flammini, Filippo Menczer. 2010

508 citations recorded by the source corpus.

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Approaches to mitigate the eﬀects of misinformation and disinformation exist, including graph-based (Ratkiewicz et al., 2011), and text-based (Castillo et al., 2013) methods.”
DeepCas: An End-to-end Predictor of Information Cascades (2016, methodology): “As the follower/followee relations are not available in the data and Twitter does not disclose the retweet paths, we follow existing work [30] and draw an edge from Twitter user A to B if either B retweeted a message of A or A mentioned B in a Tweet.”
Characterizing the 2016 Russian IRA influence campaign (2018, background): “These effects are documented by several studies Bessi and Ferrara (2016); Conover et al. (2011a); El-Khalili (2013); Ferrara (2017); Fourney et al. (2017); Ratkiewicz et al. (2011a); Shorey and Howard (2016); Woolley and Howard (2016).”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, background): “…studies (Allport and Postman, 1946; Berin-sky, 2017), management science (DiFonzo et al., 1994; Kimmel, 2004) and recently computer science and artiﬁcial intelligence (Qazvinian et al., 2011; Ratkiewicz et al., 2011; Castillo et al., 2011; Hannak et al., 2014; Zhao et al., 2015; Ma et al., 2015).”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, background): “Much research has demonstrated the promise of online and social media for use in grass roots political mobilization [13], astroturfing [14], predicting outbreaks of influenza [15], reproducing economic indices [16], crisis response [17], and measuring people ’ s happiness and mood [18,19].”
The Twitter of Babel: Mapping World Languages through Microblogging Platforms (2012, methodology): “The datased was obtained by extracting tweets from the raw Twitter Gardenhose feed [33].”
A Survey on Computational Propaganda Detection (2020, background): “…[Zhang et al., 2016], cyborgs (partially automated) [Chu et al., 2012] and troll armies (human-driven) [Linvill and Patrick, 2018], known as sockpuppets [Kumar et al., 2017], Internet water army [Chen et al., 2013], astroturfers [Ratkiewicz et al., 2011], and seminar users [Darwish et al., 2017].”
Dynamical classes of collective attention in twitter (2011, background): “an indicator of the state of mind of a population [28, 29, 30, 31, 32, 25].”
The socialbot network: when bots socialize for fame and money (2011, background): “[33] describe the use of Twitter bots to spread misinformation in the run-up to the U.”
No . 2017 . 5 Computational Propaganda in the United States of America : Manufacturing Consensus Online (2017, methodology): “There is evidence that US political actors have previously used bots in attempts to manipulate public opinion (Ratkiewicz et al., 2011a; Metaxas & Mustafaraj, 2012).”
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign (2018, background): “…of bots to attempt manipulate political communication with misinformation started in 2010, during the U.S. midterm elections, when social bots were employed to support some candidates and smear others; in that instance, bots injected thousands of tweets pointing to Web sites with fake news [45].”
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, background): “The “Truthy” system attempts a related classiﬁcation problem, whether a spreading meme is spreading “organically” or whether it is being spread by an “astroturf” campaign controlled by a single person or organization [29, 30].”