Social Media Profiling for Political Affiliation Detection

Abstract

Abstract The notion of discerning political affiliations from users’ social media behavior instills a sense of unease in many. Democracy necessitates that individuals’ political affiliations remain private, and social media challenges this foundational principle of democracy. This study uses BERT, a pre-trained language model to analyze X’s (formally Twitter) users and their political affiliations to understand that how much it is easy now to find the political affiliation of people. We collect posts in both English and Urdu languages from different political leaders and their followers, which are used to fine-tune the BERT model. The model classifies the users’ profiles into Pro, Neutral, or Anti-government classes. To assess the performance of the proposed method, experiments are conducted to evaluate its accuracy, efficiency, and effectiveness. The findings of this study confirm the hypothesis that it is easy to detect the political affiliation of individuals using social media with high accuracy (69% for English and 94% for Urdu language) and it can undermine democracy.

Keywords

• Sentiment analysis
• Political affiliation
• Social Media
• BERT
• Text classifications
• Social media monitoring