EECS/IDSS Special Seminar: Justin Cheng, "Antisocial Computing: Explaining and Predicting Negative Behavior Online"

Abstract: Antisocial behavior and misinformation are increasingly prevalent online. As users
interact with one another on social platforms, negative interactions can cascade, resulting in
complex changes in behavior that are difficult to predict. My research
introduces computational methods for explaining the causes of such negative behavior and for
predicting its spread in online communities. It complements data mining with crowdsourcing,
which enables both large-scale analysis that is ecologically valid and experiments that establish
causality. First, in contrast to past literature which has characterized trolling as confined to a
vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right
circumstances, can become trolls, and that this behavior can percolate and escalate through
a community. Second, despite prior work arguing that such behavioral and informational
cascades are fundamentally unpredictable, I demonstrate how their future growth can be
reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work
explores a future where systems can better mediate interpersonal interactions and instead
promote the spread of positive norms in communities.

Bio: Justin Cheng is a PhD candidate in the Computer Science Department at Stanford
University, where he is advised by Jure Leskovec and Michael Bernstein. His research lies at the
intersection of data science and human-computer interaction, and focuses on cascading
behavior in social networks. This work has received a best paper award, as well as several best
paper nominations at CHI, CSCW, and ICWSM. He is also a recipient of a Microsoft Research
PhD Fellowship and a Stanford Graduate Fellowship.