Daniel M. Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web (WWW '11). ACM, New York, NY, USA, 695-704. DOI=10.1145/1963405.1963503 http://doi.acm.org/10.1145/1963405.1963503
http://www.cs.cornell.edu/home/kleinber/www11-hashtags.pdf
We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the “complex contagion” principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious.
First, the process of diffusion is well-known to be governed both by influence and also by homophily — people who are linked tend to share attributes that promote similarities in behavior.
In this context there have been comparisons between the temporal patterns of expected versus un-expected information and between different media such as news
sources and blogs. Our analysis here suggests that a rich spectrum of differences may exist across topics as well.
... despite the many different styles in which people use a medium like Twitter, sociological principles such as the complex contagion of controversial topics can still be observed at the population level.
Lei Yang, Tao Sun, Ming Zhang, and Qiaozhu Mei. 2012. We know what @you #tag: does the dual role affect hashtag adoption?. In Proceedings of the 21st international conference on World Wide Web (WWW '12). ACM, New York, NY, USA, 261-270. DOI=10.1145/2187836.2187872 http://doi.acm.org/10.1145/2187836.2187872
http://www2012.wwwconference.org/proceedings/proceedings/p261.pdf
On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users.
Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before.
