Events Calendar

PhD Defense Farshad Kooti

Speaker: Farshad Kooti, PhD Candidate

Talk Title: Predicting and Modeling Human Behavioral Changes Using Digital Traces

Abstract: People are increasingly spending more time online. Finding and understanding the patterns that exist in online behavior is essential for improving user experience. One of the main characteristics of online activity is diurnal, weekly, and monthly patterns, reflecting human circadian rhythms, sleep cycles, as well as work and leisure schedules. These patterns range from mood changes reflected on Twitter at different times of the days to reading stories on news aggregator websites. Using large scale data from multiple online social networks, we uncover temporal patterns that take place at far shorter time scales. Specifically, we demonstrate short-term, within-session behavioral changes, where a session is defined as a period of time during which a person engages continuously with the online social network without a long break. On Twitter, we show that people prefer easier tasks such as retweeting over more complicated tasks such as posting an original tweet later in a session. Also, tweets posted later in a session are shorter and are more likely to contain a spelling mistake. We focus on information consumption on Facebook and show that the people spend less time reading a story as they spend more time in the session. More interestingly, the rate of the change depends on the type of the content and people are more likely to spend time on photos and videos later in the session compared to textual posts. We also found changes in the quality of the content generated on Reddit and found that comments that are posted later in a session get lower scores from other users, receive fewer replies, and have lower readability. All these findings are evidence for short-term behavioral changes in the type of activity that users perform. Moreover, we identify the factors that affect these short-term behavior changes; age of the person being the most significant factor. The trends that we found can be used to predict the online behavior of individuals with much higher accuracy than competitive baselines. E.g., we can predict the length of the activity sessions or the length of breaks on Facebook.

Our observations are compatible with the cognitive depletion theories, suggesting that people's performance drop as they perform sustained activity for a period of time, and verify small scale, laboratory studies conducted by psychologists. We also investigate more general behavioral changes than short-term behavioral changes in the context of consumer behavior, specifically online shopping and iPhone purchases. We show that there is a significant heterogeneity in these large scale datasets and not considering and handling this heterogeneity can result in false findings. We present an approach to test for the false findings using randomization and show in a case of a mistake, how it could be solved.

Host: Farshad Kooti

Location: 322

Audiences: Everyone Is Invited

Contact: Ryan Rozan