Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions

2026-08-04

AI Summary

This study systematically investigates the dual role of large language models (LLMs) in the integrity of social media content—both as powerful tools for detecting misinformation and moderating content, and as potential enablers of deceptive practices through the generation of misleading narratives, manipulation of social bots, and exacerbation of privacy breaches. Drawing on a systematic literature review of 1,048 studies published between 2019 and 2024, with in-depth analysis of 215 core papers, this work integrates multidisciplinary evidence across technical capabilities, ethical considerations, and privacy implications. It identifies critical research gaps in cross-lingual misinformation detection, real-time monitoring, and privacy-preserving mechanisms. The findings offer an empirical foundation and strategic guidance for platform governance, algorithmic design, and policy development in the age of generative AI.

Abstract

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.

Problem

• Large Language Models
• Information Integrity
• Misinformation
• Social Media
• Disinformation

Innovation

• Large Language Models
• Information Integrity
• Misinformation Detection
• Social Media
• Privacy-Preserving AI