Information-Theoretic Measures of Influence Based on Content Dynamics

TL;DR

This paper introduces a new information-theoretic measure called content transfer to quantify social influence in social media, capturing predictive content relationships without relying on detailed behavioral models.

Contribution

It presents a model-free, content-based influence measure combining entropy estimation and content representation, enabling analysis of influence beyond network links.

Findings

Content transfer captures non-trivial, predictive relationships between users.

The measure works even for users without direct social links.

It facilitates rigorous statistical causal analysis of social media influence.

Abstract

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.