Simulating Temporal User Activity on Social Networks with Sequence to Sequence Neural Models

The prediction of long-term activities of groups of users and clusters of activities around a subject in social networks is a very challenging task. In this paper, we propose a novel temporal neural network framework that tracks user engagement and activity associated with particular subjects (e.g. CVE IDs) across online platforms. The framework is able to simulate which user will do what activity and at what time. Furthermore, this framework captures groups of users reacting to an event. It also captures responses to an event on a platform and the influence of the event on activity on other platforms over time. The proposed framework aims to predict future user activity related to specific subjects across platforms. The framework also illustrates the importance influence of activities that occur on other platforms when predicting user activity for particular events on a different platform. The learned model can do simulations in a timely manner. We evaluated our user group activity prediction method on the CVE (Common Vulnerabilities and Exposures) related user groups (software vulnerability) using 3 public online social network datasets: Github, Reddit, and Twitter. Groups of users who work on a particular CVE ID are identified. Each user group has information on all users' activities related to a CVE ID. The 3 datasets from Github, Reddit, and Twitter contain more than 490,000 cross platform activities related to over 20,000 user groups (CVE IDs) from more than 50,000 users. Compared to the proposed baseline, our simulation method is better in both predictions of total activity volume over time and activity associated with an individual CVE ID.