Dynamical classes of collective attention in twitter

Abstract

Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors.

The spontaneous emergence of conventions: An experimental study of cultural evolution

“…More recently, data science approaches to studying norms have addressed many of these issues by analyzing behavior change in large online networks (34). However, these observational studies are limited by familiar problems of identification that arise from the inability to eliminate the confounding influences of institutional mechanisms.…”

“…However, these observational studies are limited by familiar problems of identification that arise from the inability to eliminate the confounding influences of institutional mechanisms. As a result, previous empirical research has been unable to identify the collective dynamics through which social conventions can spontaneously emerge (8,(34)(35)(36).…”

On Recommending Hashtags in Twitter Networks

“…We can further divide hashtags into different categories, e.g., by freshness or by topic, and study their recommendation accuracies. In [9], popular hashtags have been clustered into four categories by their before-peak, after-peak, and during-peak popularity. For each hashtag category, it will be interesting to propose different recommendation methods that work well.…”

News Dissemination on Twitter and Conventional News Channels

“…Lehmann and Cattuto (2012) worked on collective attention on Twitter and explored hashtags and the different classes of activity around their use. Their work includes a class for activity surrounding unexpected, exogenous events, characterized by a peak in hashtag usage with little activity leading up to the event.…”