Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of critical issues ranging from elections to public health, threatening the constructive exchange of information. In this talk, I will first introduce Botometer, a machine learning-based bot detection tool I built, and show how people can use it in daily life and research. I will then present the malicious bot activities I found, focusing on an example of bots spreading health misinformation during the COVID-19 pandemic. I will also cover my findings on how humans perceive social bots and how bots can serve as research instruments. Finally, I will conclude with an outlook on the future directions in light of the recent advancements of generative machine learning models.
Kai-Cheng Yang is a PhD candidate at the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. He is interested in computational social science. Particularly, he analyzes social media and medical claims data to identify inauthentic actors and adverse behaviors and studies their implications for the online information ecosystem and public health. He develops computational methods by combining data science, machine learning, and network science with domain knowledge and social science theories. These methods have been converted into publicly available tools (e.g., Botometer) that facilitate the work of tens of thousands of users. His work appeared in various academic journals, including Nature Communications, New Media & Society, JAMA Network Open, and in the proceedings of conferences such as AAAI, ICWSM, and CIKM. His research was also covered by CNN, BBC, and The New York Times.