Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum
This work addresses the problem of understanding public opinion on social media for researchers in computational social science, though it is incremental as it applies existing methods to a new dataset.
The study analyzed tweets about the 2015 Irish Marriage Referendum to integrate sentiment and social network structure, finding that user sentiment in mentions correlates with received mentions and that connections are more common among users with similar sentiment scores, with applications in opinion dynamics and polling.
We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags #marref and #marriageref that was posted in the days leading to the referendum, and construct networks to aggregate sentiment and use it to study the interactions among users. Our results show that the sentiment of mention tweets posted by users is correlated with the sentiment of received mentions, and there are significantly more connections between users with similar sentiment scores than among users with opposite scores in the mention and follower networks. We combine the community structure of the two networks with the activity level of the users and sentiment scores to find groups of users who support voting `yes' or `no' in the referendum. There were numerous conversations between users on opposing sides of the debate in the absence of follower connections, which suggests that there were efforts by some users to establish dialogue and debate across ideological divisions. Our analysis shows that social structure can be integrated successfully with sentiment to analyse and understand the disposition of social media users. These results have potential applications in the integration of data and meta-data to study opinion dynamics, public opinion modelling, and polling.