Dynamical Classes of Collective Attention in Twitter

TL;DR

This paper investigates the temporal dynamics of hashtag popularity on Twitter, identifying distinct classes of collective attention and analyzing their relation to social and external factors.

Contribution

It introduces a classification of hashtag popularity dynamics and links these classes to the nature of the underlying events, highlighting the limited role of epidemic spreading.

Findings

Hashtag popularity follows discrete dynamical classes.

External factors predominantly drive hashtag popularity.

Epidemic spreading has a minor influence on hashtag propagation.

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 hastag 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 hastag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hastag popularity, which is mostly driven by exogenous factors.