The Social Media Genome: Modeling Individual Topic-Specific Behavior in Social Media

TL;DR

This paper introduces a novel 'genotype' model capturing individual user behavior on social media across topics, improving influence prediction and information spread strategies by incorporating dynamic, topic-specific user interests and influence structures.

Contribution

It proposes the 'genotype' model for user behavior, demonstrating its invariance within topics and its effectiveness in influence prediction and spread minimization.

Findings

Genotypes remain invariant within topics.

Influence backbones differ from static follower networks.

Over 20% improvement in influence prediction accuracy.

Abstract

Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on information adoption and show that they differ significantly from the static follower network. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than $20% improvement, compared to purely structural features. We also demonstrate that knowledge of user genotypes and influence backbones allow for the design of effective strategies for latency minimization of topic-specific information spread.