Description
Under our AI for Good initiative, KUNGFU.AI has analyzed tweets related to the Coronavirus/COVID-19 pandemic in order to find possible patterns of disinformation and unusual propagation of content on Twitter. In separate time periods starting in early February 2020, we have analyzed more than 26 M tweets that mention Coronavirus and/or COVID-19 using the free Twitter public API. We used information entropy measures calculated from both the text content and the timing of the tweets to identify accounts displaying significantly more automation than normal Twitter users. To date, we have identified more than 4500 such highly automated accounts. We then applied network graph analysis techniques to determine which automated accounts were most successful in getting their content shared or retweeted.
Instructor's Bio
Dr. Steve Kramer, Chief Scientist of KUNGFU.AI
Dr. Steve Kramer, Chief Scientist of KUNGFU.AI, is a computational physicist and data science entrepreneur with 27+ years of post-PhD experience in data science, research, software, and business management. He earned a Ph.D. in physics in the Center for Nonlinear Dynamics at The University of Texas at Austin in 1993. Dr. Kramer has extensive research experience spanning data mining, machine learning, anomaly detection, bot/cyborg detection, clustering, network graph analysis, deep learning, spatiotemporal forecasting, predictive analytics, social media analytics, and pattern discovery/recognition. In 2014, he patented a robust method for dynamic anomaly detection based on chaos theory. Steve spoke at Data Day Texas in 2014 and 2018 and at Data Day Seattle in 2016. Since 2011, he has served as a program committee member and reviewer for the ACM KDD and IEEE Security and Intelligence Informatics conferences.
Recording
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Identifying viral bots and cyborgs in social media
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Video recording
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