Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Spatial-temporal causal modeling for climate change attribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relationship identification for social network discovery
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Divergence estimation for multidimensional densities via k-nearest-neighbor distances
IEEE Transactions on Information Theory
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Mark my words!: linguistic style accommodation in social media
Proceedings of the 20th international conference on World wide web
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Extracting social power relationships from natural language
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Influence and passivity in social media
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Information transfer in social media
Proceedings of the 21st international conference on World Wide Web
Echoes of power: language effects and power differences in social interaction
Proceedings of the 21st international conference on World Wide Web
Learning social network embeddings for predicting information diffusion
Proceedings of the 7th ACM international conference on Web search and data mining
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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.