Digital Traces of Interest: Deriving Interest Relationships from Social Media Interactions

Abstract

Facebook and Twitter have changed the way we consume information, allowing the people we follow to become our “social filters” and determine the content of our information stream. The capability to discover the individuals a user is most interested in following has therefore become an important aspect of the struggle against information overflow. We argue that the people users are most interested in following are not necessarily those with whom they are most familiar. We compare these two types of social relationships – interest and familiarity – inside IBM. We suggest inferring interest relationships from users’ public interactions on four enterprise social media applications. We study these interest relationships through an offline analysis as well as an extensive user study, in which we combine people-based and content-based evaluations. The paper reports a rich set of results, comparing various sources for implicit interest indications distinguishing between content-related activities and status or network updates, showing that the former are of more interest and highlighting that the interest relationships include very interesting individuals that are not among the most familiar ones, and can therefore play an important role in social stream filtering, especially for content-related activities.