Dynamic Data Capture from Social Media Streams: A Contextual Bandit Approach

Abstract

Social Media usually provide streaming data access that enable dynamic capture of the social activity of their users. Leveraging such APIs for collecting social data that satisfy a given pre-defined need may constitute a complex task, that implies careful stream selections. With user-centered streams, it indeed comes down to the problem of choosing which users to follow in order to maximize the utility of the collected data w.r.t. the need. On large social media, this represents a very challenging task due to the huge number of potential targets and restricted access to the data. Because of the intrinsic non-stationarity of user's behavior, a relevant target today might be irrelevant tomorrow, which represents a major difficulty to apprehend. In this paper, we propose a new approach that anticipates which profiles are likely to publish relevant contents - given a predefined need - in the future, and dynamically selects a subset of accounts to follow at each iteration. Our method has the advantage to take into account both API restrictions and the dynamics of users' behaviors. We formalize the task as a contextual bandit problem with multiple actions selection. We finally conduct experiments on Twitter, which demonstrate the empirical effectiveness of our approach in real-world settings.