FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds
This addresses the need for transparency and explainability in social media feeds for users concerned about filter bubbles and privacy, though it is an incremental improvement on existing explanation methods.
The paper tackles the problem of explaining why certain items appear in a user's social media feed by developing FAIRY, a framework that discovers, ranks, and explains relationships between user actions and feed items using an interaction graph and learning-to-rank model, with user studies demonstrating its practical viability and benefits.
Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.