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arXiv · 1604.01105

Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds

Amit Sharma·Dan Cosley
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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.

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Amit Sharma, Dan Cosley. 2016-04-05. Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds. https://doi.org/10.1145/2818048.2819982

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