Challenge: generative language agents predict user behaviors such as liking, sharing, and flagging content.
Approach: They propose a framework where generative language agents predict user behaviors such as liking, sharing, and flagging content.
Outcome: The proposed framework analyzes content moderation strategies and user engagement dynamics at scale and demonstrates that agents’ articulated reasoning for their social interactions aligns with their collective engagement patterns.

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Don’t Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism (2026.eacl-long)

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Challenge: Social media platforms face mounting regulatory pressure worldwide . obtaining evidence regarding platform risks remains challenging .
Approach: They propose a formal framework for simulation of social networks before focusing on imitating user communication.
Outcome: The proposed model can replicate human behavior with sufficient realism to perform the task.
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)

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Challenge: Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges.
Approach: They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs.
Outcome: The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges .
GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation (2026.acl-long)

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Challenge: Social graphs provide high-quality supervision signals that encode local interactions and global network structure, yet they remain underutilized for LLM training.
Approach: They propose a general LLM-based social graph simulation framework that leverages graph data as supervision for LLM training.
Outcome: The proposed framework improves micro-level alignment by 6.1% on three real-world networks compared to the strongest baseline.
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)

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Challenge: Existing methods for simulating social movements encounter challenges in capturing behavior of participants.
Approach: They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users.
Outcome: The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility.
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

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Challenge: Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach: They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome: The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)

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Challenge: Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models.
Approach: They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups.
Outcome: The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech.
Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions (2026.eacl-long)

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Challenge: Existing challenges in misinformation exposure and susceptibility vary across demographics.
Approach: They propose a framework that investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformation.
Outcome: The proposed framework analyzes the spread of misinformation under persuasion among demographic-oriented LLM agents.
MisinfoEval: Generative AI in the Era of “Alternative Facts” (2024.emnlp-main)

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Challenge: Existing efforts to address misinformation on social media platforms are hampered by user biases and scalability challenges.
Approach: They propose a framework for generating and comprehensively evaluating large language model based misinformation interventions using a simulated social media environment and personalized explanations tailored to users' beliefs.
Outcome: The proposed framework improves accuracy at reliability labeling by up to 41.72% and personalized explanations appeal to users' pre-existing values.
Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025.findings-emnlp)

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Challenge: Current studies rely on simplistic user and network modeling and neglect dynamic behavior of bots.
Approach: They propose a multi-agent-based framework for disinformation dissemination . it incorporates both malicious and legitimate bots and allows quantitative evaluation of correction strategies.
Outcome: The proposed framework incorporates both malicious and legitimate bots and their controlled dynamic participation allows for quantitative analysis of correction strategies.
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)

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Challenge: Social media platforms have enabled large-scale influence campaigns, impacting democratic processes.
Approach: They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario.
Outcome: The proposed system improves diversity and realism while ensuring its adherence to the original scenario.

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