Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds

By Amit Sharma, Dan Cosley. 2016

15 citations recorded by the source corpus.

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.

Subfields: Information Systems, Statistical and Nonlinear Physics

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, neutral): “In addition to identifying natural experiments (where assignment to treatment is random or “as good as random”), there are methods that help assess causation in observational studies and mitigate the effects of confounding or selection bias, under strong assumptions; including matched analysis (De C”