A Qualitative Examination of Topical Tweet and Retweet Practices
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TL;DR
A clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself is found; suggesting the need to study content properties in link-based diffusion models.
Abstract
This work contributes to the study of retweet behavior on Twitter surrounding real-world events. We analyze over a million tweets pertaining to three events, present general tweet properties in such topical datasets and qualitatively analyze the properties of the retweet behavior surrounding the most tweeted/viral content pieces. Findings include a clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself; suggesting the need to study content properties in link-based diffusion models.