Analyzing voter behavior on social media during the 2020 US presidential election campaign.

Résumé

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users' publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.