Information credibility on twitter

We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources. We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.

[1]  Eni Mustafaraj,et al.  From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search , 2010 .

[2]  Danah Boyd,et al.  Detecting Spam in a Twitter Network , 2009, First Monday.

[3]  Hanan Samet,et al.  TwitterStand: news in tweets , 2009, GIS.

[4]  Ana-Maria Popescu,et al.  Detecting controversial events from twitter , 2010, CIKM.

[5]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[6]  Cory L. Armstrong,et al.  Blogs of Information: How Gender Cues and Individual Motivations Influence Perceptions of Credibility , 2009, J. Comput. Mediat. Commun..

[7]  Bertrand De Longueville,et al.  "OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires , 2009, LBSN '09.

[8]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[9]  D. Watts,et al.  Viral marketing for the real world , 2007 .

[10]  D. Watts,et al.  Viral Marketing for the Real World Duncan J. Watts, Jonah Peretti, and Michael Frumin , 2007 .

[11]  Meredith Ringel Morris,et al.  Augmenting web pages and search results to support credibility assessment , 2011, CHI.

[12]  B. J. Fogg,et al.  The elements of computer credibility , 1999, CHI '99.

[13]  Omar Alonso,et al.  Detecting Uninteresting Content in Text Streams , 2010 .

[14]  Kirill Kireyev.  Applications of Topics Models to Analysis of Disaster-Related Twitter Data , 2009 .

[15]  Leysia Palen,et al.  Chatter on the red: what hazards threat reveals about the social life of microblogged information , 2010, CSCW '10.

[16]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[17]  Nick Koudas,et al.  TwitterMonitor: trend detection over the twitter stream , 2010, SIGMOD Conference.

[18]  Miriam J. Metzger,et al.  Perceptions of Internet Information Credibility , 2000 .

[19]  Mor Naaman,et al.  Is it really about me?: message content in social awareness streams , 2010, CSCW '10.

[20]  Nello Cristianini,et al.  Flu Detector - Tracking Epidemics on Twitter , 2010, ECML/PKDD.

[21]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[22]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.

[23]  Barbara Poblete,et al.  Twitter under crisis: can we trust what we RT? , 2010, SOMA '10.

[24]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[25]  Virgílio A. F. Almeida,et al.  Detecting Spammers on Twitter , 2010 .

[26]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[27]  P. Earle,et al.  OMG Earthquake! Can Twitter Improve Earthquake Response? , 2009 .

[28]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[29]  Thomas J. Johnson,et al.  Every Blog Has Its Day: Politically-interested Internet Users' Perceptions of Blog Credibility , 2007, J. Comput. Mediat. Commun..

[30]  Miriam J. Metzger,et al.  behaviors on the perceived credibility of web-based information The role of site features, user attributes, and information verification , 2007 .

[31]  Mike Schmierbach,et al.  A Little Bird Told Me, So I Didn't Believe It: Twitter, Credibility, and Issue Perceptions , 2012 .

[32]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[33]  Jacob Ratkiewicz,et al.  Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams , 2010, ArXiv.