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

## TL;DR

FAIRY is a framework that explains the relationship between users' actions and their social media feeds by modeling interaction graphs and ranking explanatory paths, enhancing transparency and user understanding.

## Contribution

The paper introduces FAIRY, a novel framework that systematically discovers and ranks explanations for social media feed items based on user interaction graphs.

## Key findings

- User studies show FAIRY improves understanding of feed content.
- FAIRY effectively identifies relevant explanation paths.
- The framework enhances transparency in social media feeds.

## Abstract

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.