AI 中文总结
本综述分析LLMs对社交媒体信息完整性的双重影响，指出其可增强恶意内容检测能力但也会生成欺骗性内容，提出跨语言检测等研究方向以利用LLMs并降低风险。
AI 中文摘要
大语言模型（LLMs）已成为影响社交媒体平台信息完整性的强大工具。本综述全面考察了LLMs在促进和缓解各类信息完整性挑战方面的双重作用，这些挑战包括错误信息、虚假信息、假新闻、社交机器人及隐私问题。我们对2019年至2024年的文献进行了全面综述，筛选了1048项研究，并对215篇代表性论文进行了深入分析。这种系统方法使我们能够识别LLMs影响社交媒体生态系统信息安全的关键模式。通过对多个数据库论文的系统分析，研究结果显示，LLMs虽可增强恶意内容的检测能力并实现复杂的防御机制，但同时也会带来风险，因为它们能生成极具说服力的欺骗性内容。我们对信息完整性不同维度的潜力与挑战进行了分类和分析，考察了技术能力、伦理影响及隐私问题。研究指出了当前方法中的关键差距，特别是跨语言检测、实时监控和隐私保护实现方面。最后，我们提出了未来的研究方向，并为利益相关者提供建议，以在利用LLMs的同时降低社交媒体信息完整性方面的风险。
英文摘要
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.
CommentsIt has been accepted by Computing Surveys. Preview From: htong@illinois.edu Congratulations! Your manuscript, "Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions," has been accepted for publication in ACM Computing Surveys.Your paper will be returned to your Author Center. Dr. Hanghang Tong Editor-in-Chief ACM Computing Surveys
Journal refJust accpeted by ACM Computing Surveys 2026
DOI：10.1145/3839233
