A User Modeling Pipeline for Studying Polarized Political Events in Social Media

TL;DR

This paper introduces a comprehensive user modeling pipeline to analyze social media discussions on polarized political events, focusing on demographics, spam filtering, and opinion prediction, tested on Catalonia and Lombardy referendums.

Contribution

The paper presents a novel pipeline combining user filtering, demographic extraction, and political polarity prediction for social media analysis of political events.

Findings

Effective in predicting political trends for Catalonia

Less effective for Lombardy due to demographic representation issues

Twitter users more representative of opposition than supporters

Abstract

This paper presents a user modeling pipeline to analyze discussions and opinions shared on social media regarding polarized political events (e.g., public polls). The pipeline follows a four-step methodology. First, social media posts and users metadata are crawled. Second, a filtering mechanism is applied to filter spammers and bot users. As a third step, demographics information is extracted out of the valid users, namely gender, age, ethnicity and location information. Finally, the political polarity of the users with respect to the analyzed event is predicted. In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users. Experiments show that the method was effective in predicting the political trends for the Catalonia case, but not for the Lombardy case. Among the various motivations for this, we noticed that in general Twitter was more representative of the users opposing the referendum than the ones in favor.