Ideological orientation and extremism detection in online social networking sites: A systematic review

Abstract

The rise of social networking sites has reshaped digital interactions, becoming fertile grounds for extremist ideologies, notably in the United States. Despite previous research, understanding and tackling online ideological extremism remains challenging. In this context, we conduct a systematic literature review to comprehensively analyze existing research and offer insights for both researchers and policymakers. Spanning from 2005 to 2023, our review includes 110 primary research articles across platforms like Twitter (X), Facebook, Reddit, TikTok, Telegram, and Parler. We observe a diverse array of methodologies, including natural language processing (NLP), machine learning (ML), deep learning (DL), graph-based methods, dictionary-based methods, and statistical approaches. Through synthesis, we aim to advance understanding and provide actionable recommendations for combating ideological extremism effectively on online social networking sites.

Keywords

• Extremism detection
• Machine learning
• Natural language processing
• Predictive models
• Social media
• User-generated content