Online social interactions have become an integral part of people's lives, e.g., presidential candidates use Facebook and Twitter to engage with the public, programmers rely on Stackoverflow to write code, and various communities have been forming online. This unprecedented amount of social interaction offers tremendous opportunities to understand human behavior. Such an understanding can induce significant social impact, ranging from influencing election outcomes to better communication for everyone.
My research leverages newly available massive datasets of social interactions to understand human behavior and predict human decisions. These results can be used to build or improve socio-technical systems. In this talk, I will explain my research at both micro and macro levels. At the micro level, I investigate the effect of wording in message sharing via natural experiments. I develop a classifier that outperforms humans in predicting which tweet will be retweeted more. At the macro level, I examine how users engage with multiple communities and find that, surprisingly, users continually explore new communities on Reddit. Moreover, their exploration patterns in their early "life" can be used to predict whether they will eventually abandon Reddit. I will finish with some discussion of future research directions in understanding human behavior.
