Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds

TL;DR

This paper presents a statistical method to accurately measure social influence in online activity feeds, revealing that actual influence is minimal and often overestimated by previous methods.

Contribution

It introduces a new approach that distinguishes social influence from homophily, providing more accurate estimates of copy-influence in social networks.

Findings

Copy-influence accounts for less than 1% of user actions.

Previous estimates significantly overstate social influence effects.

Most user actions are unaffected by activity feeds.

Abstract

Many online social networks thrive on automatic sharing of friends' activities to a user through activity feeds, which may influence the user's next actions. However, identifying such social influence is tricky because these activities are simultaneously impacted by influence and homophily. We propose a statistical procedure that uses commonly available network and observational data about people's actions to estimate the extent of copy-influence---mimicking others' actions that appear in a feed. We assume that non-friends don't influence users; thus, comparing how a user's activity correlates with friends versus non-friends who have similar preferences can help tease out the effect of copy-influence. Experiments on datasets from multiple social networks show that estimates that don't account for homophily overestimate copy-influence by varying, often large amounts. Further, copy-influence estimates fall below 1% of total actions in all networks: most people, and almost all actions, are not affected by the feed. Our results question common perceptions around the extent of copy-influence in online social networks and suggest improvements to diffusion and recommendation models.