title: In the mood: the dynamics of collective sentiments on Twitter.
abstract: We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset.
authors.affiliation: CountingLab Ltd, Reading, UK; Centre for the Mathematics of Human Behaviour, Department of Mathematics and Statistics, University of Reading, Reading, UK.
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Charlton
authors.affiliation: CountingLab Ltd, Reading, UK; Centre for the Mathematics of Human Behaviour, Department of Mathematics and Statistics, University of Reading, Reading, UK.
authors.forename: Colin
authors.initials: C
authors.lastname: Singleton
authors.affiliation: Centre for the Mathematics of Human Behaviour, Department of Mathematics and Statistics , University of Reading , Reading, UK.
authors.forename: Danica Vukadinović
authors.initials: DV
authors.lastname: Greetham
keywords: Twitter communities; agent-based modelling; communicability; dynamics of collective emotions; evolving networks