Predicting and modeling human behavioral changes using digital traces|社交媒体行为分析数据集|消费者行为研究数据集

People are increasingly spending more time online. Understanding how this time is spent and what patterns exist in online behavior is essential for improving systems and 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 and days of the weeks 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 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 a 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. We find that other factors such as gender, location, and time of the day also have considerable role in the behavioral changes. All these correlations can be used to predict the online behavior of individuals with high accuracy. E.g., we can predict the length of the activity session or the break on Facebook with much higher accuracy than other competitive baselines. Our observations are compatible with the cognitive depletion theories that suggest 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. We analyze data from purchases that people made online, including purchasing goods, taking rides with ride-sharing apps, and purchases from Apple's app store. We show that there is a significant heterogeneity in these large-scale data sets and not 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 the mistake could be solved.