  The fundamental building block of social influence is for one person to
elicit a response in another. Researchers measuring a "response" in social
media typically depend either on detailed models of human behavior or on
platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most
content on social networks is difficult to model because the modes and
motivation of human expression are diverse and incompletely understood. We
introduce content transfer, an information-theoretic measure with a predictive
interpretation that directly quantifies the strength of the effect of one
user's content on another's in a model-free way. Estimating this measure is
made possible by combining recent advances in non-parametric entropy estimation
with increasingly sophisticated tools for content representation. We
demonstrate on Twitter data collected for thousands of users that content
transfer is able to capture non-trivial, predictive relationships even for
pairs of users not linked in the follower or mention graph. We suggest that
this measure makes large quantities of previously under-utilized social media
content accessible to rigorous statistical causal analysis.
