  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.
