The Attention Automaton: Sensing Collective User Interests in Social Network Communities
The vast quantity of information shared in social networks has brought us to an age of attention scarcity, where getting users to be attentive to a message is not a given. In fact, it has become the limiting factor in the consumption of information by end users. Understanding what captures the collective attention within a community of users in a social network is invaluable to many applications, such as product marketing, targeted advertising and social or political campaign organization. Several scholars have analyzed how information spreads in social networks under the constraint of attention. However, few papers provide a quantitative method to model and predict attention at every instant in the dynamic social web. In this paper, we propose the Attention Automaton, a probabilistic finite automata that can estimate the collective attention of some user community. Communities are based on geographical vicinity of users or having common interests (like followers of a given account) on Twitter. We identify two key factors that drive collective user attention: (1) the attention volatility of the community (frequency of change of trending topics), and (2) the selective categorical affinity of the user group towards certain trends. Our results, which are based on a eight-month dataset of Twitter trending topics across 111 geographic regions and audience trends of approximately 50 brands indicate that the proposed Attention Automaton can predict audience reception of impending trends based on categorical filters and inherent oscillations in user activity.