Information Flow on Social Networks: From Empirical Data to Situation Understanding

This paper describes characteristics of information flow on social channels, as a function of content type and relations among individual sources, distilled from analysis of Twitter data as well as human subject survey results. The working hypothesis is that individuals who propagate content on social media act (e.g., decide whether to relay information or not) in accordance with their understanding of the content, as well as their own beliefs and trust relations. Hence, the resulting aggregate content propagation pattern encodes the collective content interpretation of the underlying group, as well as their relations. Analysis algorithms are described to recover such relations from the observed propagation patterns as well as improve our understanding of the content itself in a language agnostic manner simply from its propagation characteristics. An example is to measure the degree of community polarization around contentious topics, identify the factions involved, and recognize their individual views on issues. The analysis is independent of the language of discourse itself, making it valuable for multilingual media, where the number of languages used may render language-specific analysis less scalable.