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.
Heather Roy, Tarek Abdelzaher, Elizabeth K. Bowman, and Md. Tanvir Al Amin, "Information flow on social networks: from empirical data to situation understanding," Proc. SPIE 10207, Next-Generation Analyst V, 1020702 (Presented at SPIE Defense + Security: April 10, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2266585.
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