Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum

TL;DR

This study analyzes Twitter data surrounding the 2015 Irish Marriage Referendum to understand how social structure and sentiment interact, revealing patterns of support, opposition, and cross-ideological dialogue among users.

Contribution

It introduces a method to combine sentiment analysis with social network structure to identify user groups and interaction patterns related to referendum opinions.

Findings

Users' sentiment in tweets correlates with received mentions.

Users with similar sentiment are more connected than those with opposing views.

Cross-ideological conversations occurred even without direct follower links.

Abstract

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.