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

arXiv:2608.04375 · cs.CR · Submitted 2026-08-05 · Read on arXiv

Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu, Chi Zhang, Changjia Zhu, Ning Wang, Mingkui Wei, Zhuo Lu, Yao Liu, Lingyao Li

cs.CR

Submitted: 2026-08-05

Updated: 2026-08-11

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 Surveys.Your paper will be returned to your Author Center. Dr. Hanghang Tong Editor-in-Chief ACM Computing Surveys

Journal ref: Just accpeted by ACM Computing Surveys 2026

DOI: 10.1145/3839233

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Verification — 39× in this paper · explained in 2 episodes
• Accuracy — 27× in this paper · explained in 1 episode
• Benchmark — 26× in this paper · explained in 2 episodes
• Transformer — 26× in this paper · explained in 2 episodes
• Accountability — 14× in this paper · explained in 2 episodes
• Papers — 14× in this paper · explained in 3 episodes
• Federated Learning — 13× in this paper · explained in 19 episodes
• Hallucination — 13× in this paper · explained in 4 episodes
• Robustness — 13× in this paper · explained in 6 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Transformers — 12× in this paper · explained in 2 episodes
• Differential Privacy — 9× in this paper · explained in 5 episodes
• Membership Inference — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Core — 7× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Memorization — 7× in this paper · explained in 4 episodes
• Decoder — 6× in this paper · explained in 2 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 6× in this paper
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Actor — 5× in this paper · explained in 3 episodes
• Explainability — 5× in this paper · explained in 4 episodes
• Knowledge Distillation — 5× in this paper · explained in 15 episodes
• Meeting of the Association for Computational Linguistics — 5× in this paper
• Provenance — 5× in this paper · explained in 5 episodes
• Steganography — 5× in this paper · explained in 2 episodes
• Augmentation — 4× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 4 episodes
• Evaluation Protocol — 4× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Human-in-the-loop — 4× in this paper · explained in 4 episodes
• Low-Resource Languages — 4× in this paper · explained in 4 episodes
• Prompt Engineering — 4× in this paper · explained in 6 episodes
• Trace — 4× in this paper · explained in 2 episodes
• BERT Model — 3× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 9 episodes
• Differentially Private — 3× in this paper · explained in 3 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Empirical Methods in Natural Language Processing — 3× in this paper
• Evaluation Framework — 3× in this paper · explained in 3 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Language Models (LLMs) — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 9 episodes
• Polarization — 3× in this paper · explained in 2 episodes
• Transformer Model — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Active Learning — 2× in this paper · explained in 6 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Conference on Data Science and Advanced Analytics — 2× in this paper
• Continual Learning — 2× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 7 episodes
• EMMA — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 4 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Federated Learning (FL) — 2× in this paper · explained in 12 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Hierarchical Classification — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Meta-learning — 2× in this paper · explained in 3 episodes
• Obfuscation — 2× in this paper · explained in 2 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Absorb — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• California Consumer Privacy Act — 1× in this paper
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Concept Drift — 1× in this paper · explained in 6 episodes
• Contrastive Learning — 1× in this paper · explained in 9 episodes
• Covariate Shift — 1× in this paper · explained in 3 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Data Leakage — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Faithfulness — 1× in this paper · explained in 2 episodes
• False Negatives — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 3 episodes
• Feedback Loop — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Harness — 1× in this paper · explained in 2 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Homogenization — 1× in this paper · explained in 2 episodes
• Hybrid Approaches — 1× in this paper · explained in 2 episodes
• Hybrid Architecture — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Hybrid Model — 1× in this paper · explained in 2 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• Information Leakage — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 8 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Long-horizon reasoning — 1× in this paper · explained in 2 episodes
• Methodological Rigor — 1× in this paper · explained in 2 episodes

Sources

• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• Public sentiment analysis and topic modeling regarding ChatGPT in mental health on Reddit: Negative sentiments increase over time
• CoAID: COVID-19 Healthcare Misinformation Dataset
• Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
• Understanding Transformers for Bot Detection in Twitter
• Prospects for inconsistency detection using large language models and sheaves
• Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis
• Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
• Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
• Large Language Model Agent for Fake News Detection
• Staying vigilant in the Age of AI: From content generation to content authentication
• The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
• Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
• Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media
• Detecting value-expressive text posts in Russian social media
• Carbon Emissions and Large Neural Network Training
• Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
• Undesirable Memorization in Large Language Models: A Survey
• Crowd Intelligence for Early Misinformation Prediction on Social Media

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