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Users increasingly rely on social media feeds for consuming dailyinformation. The items in a feed, such as news, questions, songs, etc., usuallyresult from the complex interplay of a user's social contacts, her interestsand her actions on the platform. The relationship of the user's own behaviorand the received feed is often puzzling, and many users would like to have aclear explanation on why certain items were shown to them. Transparency andexplainability are key concerns in the modern world of cognitive overload,filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, aframework that systematically discovers, ranks, and explains relationshipsbetween users' actions and items in their social media feeds. We model theuser's local neighborhood on the platform as an interaction graph, a form ofheterogeneous information network constructed solely from information that iseasily accessible to the concerned user. We posit that paths in thisinteraction graph connecting the user and her feed items can act as pertinentexplanations for the user. These paths are scored with a learning-to-rank modelthat captures relevance and surprisal. User studies on two social platformsdemonstrate the practical viability and user benefits of the FAIRY method.

FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds