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

Computer Science > Cryptography and Security [Submitted on 5 Aug 2026] Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu, Chi Zhang, Changjia Zhu, Ning Wang, Mingkui Wei, Zhuo Lu, Yao Liu, Lingyao Li 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. Comments: It 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 this http URL paper will be returned to your Author Center. Dr. Hanghang Tong Editor-in-Chief ACM Computing Surveys Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.04375 [cs.CR]   (or arXiv:2608.04375v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.04375 Focus to learn more Journal reference: Just accpeted by ACM Computing Surveys 2026 Submission history From: Junjie Xiong [view email] [v1] Wed, 5 Aug 2026 02:32:43 UTC (3,316 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)