As social media become major channels for the diffusion of news and information, they are also increasingly attractive and targeted for abuse and manipulation. This talk overviews ongoing network analytics, data mining, and modeling efforts to understand the spread of misinformation online and offline. I present machine learning methods to detect astroturf, social bots, and promotion campaigns, and outline initial steps toward computational fact checking, as well as theoretical models to study how truthful and truthy facts compete for our collective attention. These efforts will be framed by a case study in which, ironically, our own research became the target of a coordinated disinformation campaign. The work presented in this talk was carried out in collaborations with current and past students, postdocs, and colleagues in the Center for Complex Networks and Systems Research at Indiana University (cnets.indiana.edu). This research is supported in part by the National Science Foundation (grant CCF-1101743), DARPA (grant W911NF-12-1-0037), and the J. S. McDonnell Foundation (grant 220020274).
Opinions, findings, and conclusions or recommendations in this material are those of the author and do not necessarily reflect the views of these funding agencies.
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