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In the mood: the dynamics of collective sentiments on Twitter

Charlton, Nathaniel; Singleton, Colin and Greetham, Danica Vukadinović (2016). In the mood: the dynamics of collective sentiments on Twitter. Royal Society Open Science, 3(6), article no. 160162.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1098/rsos.160162
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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.

Item Type: Journal Item
Copyright Holders: 2016 The Authors
ISSN: 2054-5703
Keywords: evolving networks; Twitter communities; dynamics of collective emotions; communicability; agent-based modelling
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 55697
Depositing User: Danica Vukadinovic Greetham
Date Deposited: 11 Feb 2019 16:33
Last Modified: 17 Jun 2019 19:19
URI: http://oro.open.ac.uk/id/eprint/55697
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