Abstract
With the widespread use of Internet technologies, online behaviors play a more and more important role in humans’ daily lives. Knowing the times when humans perform their next online activities can be quite valuable for developing better online services, which prompts us to wonder whether the times of users’ next online activities are predictable. In this paper, we investigate the temporal predictability in human online activities through exploiting the dataset from the social network Foursquare. Through discretizing the inter-event times of users’ Foursquare activities into symbols, we map each user’s inter-event time sequence to a sequence of inter-event time symbols. By applying the information-theoretic method to the sequences of inter-event time symbols, we show that for a user’s Foursquare activities, knowing the time interval between the current activity and the previous activity decreases the entropy of the time interval between the next activity and current activity, i.e., the time of the user’s next Foursquare activity is predictable. Much of the predictability is explained by the equal-interval repeat; that is, users perform consecutive Foursquare activities with approximately equal time intervals. On the other hand, the unequal-interval preference, i.e., the preference of performing Foursquare activities with a fixed time interval after another given time interval, is also an origin for predictability. Furthermore, our results reveal that the Foursquare activities on weekdays have a higher temporal predictability than those on weekends and that users’ Foursquare activity is more temporally predictable if his/her previous activity is performed in a location that he/she visits more frequently.
Highlights
Understanding human activities has been considered a long-term fundamental and vital task for decades
We explore the temporal predictability in human online activities using the dataset collected from the social network Foursquare
Our results show that for Foursquare activities, knowing the time interval between the current activity and the previous activity decreases the entropy of the time interval between the activity and the current activity, which indicates that the time of the Foursquare activity is predictable
Summary
Understanding human activities has been considered a long-term fundamental and vital task for decades. Differing from activities in real life, humans can perform nearly any online activity in any place at any time due to the high coverage of Internet access and the mass popularization of smart devices (e.g., smart phone, smart pad), i.e., humans face much fewer constraints when performing activities on the web than in real life This difference raises the question of whether humans’ online activities are predictable as well. For the commenting partner as well as choosing the location of online check-ins, it is shown that knowing the current action can decrease the uncertainty about the one [17,19] It is shown in [20,21] that users’ rating and browsing trajectories on websites have a high degree of predictability. It is revealed that Foursquare activities on weekdays have a higher temporal predictability than those on weekends and that the user’s Foursquare activity is more temporally predictable if the previous one was performed in locations with a higher visit frequency
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