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This review analyzes the dual role of Large Language Models (LLMs) in shaping information integrity on social media, highlighting their potential to both enhance detection of malicious content and facilitate the spread of misinformation. By systematically reviewing 1,048 studies and conducting an in-depth analysis of 215 key papers, the authors identify critical gaps in existing methodologies, particularly in areas such as cross-lingual detection and privacy-preserving techniques. The findings underscore the urgent need for future research to address these challenges while leveraging LLM capabilities to improve information security on social media platforms.
LLMs can both combat misinformation and generate it, revealing a paradox that demands urgent research attention.
Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in both facilitating and mitigating various information integrity challenges, including misinformation, disinformation, fake news, social bots, and privacy concerns. \textcolor{black}{We conduct a comprehensive review of the literature from 2019 to 2024, screening 1048 studies and performing an in-depth analysis of 215 representative papers. This systematic approach allows us to identify key patterns in how LLMs influence the information security in social media ecosystems.} Through a systematic analysis of papers from multiple databases, our findings reveal that while LLMs can enhance detection capabilities for malicious content and enable sophisticated defense mechanisms, they simultaneously pose risks by enabling the generation of highly convincing, deceptive content. We categorize and analyze the potential and challenges across different dimensions of information integrity, examining technical capabilities, ethical implications, and privacy concerns. The study demonstrates critical gaps in current approaches, particularly in cross-lingual detection, real-time monitoring, and privacy-preserving implementations. We conclude by proposing future research directions and recommendations for stakeholders to leverage LLMs while mitigating risks in social media information integrity.
