Long trend dynamics in social media
Department of Applied Physics, Stanford University, Stanford, CA, USA
2 Social Computing Lab, HP Labs, Palo Alto, California, USA
Accepted: 18 May 2012
Published online: 18 May 2012
A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. By introducing a stochastic dynamical model that takes into account the user’s repeated involvement with given topics, we can predict the distribution of trend durations as well as the thresholds in popularity that lead to their emergence within social media. Detailed measurements of datasets from Twitter confirm the validity of the model and its predictions.
© The Author(s), 2012