arXiv:2510. 19299v2 Announce Type: replace Abstract: Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily, reciprocity, and social validation -- and what memory and learning mechanisms enable such dynamics to emerge?
By Philipp J. Schneider, Lin Tian, Marian-Andrei Rizoiu
The paper introduces a controlled testbed to study how goal‑directed persuaders shift stances in networks of large language model agents, using real‑world ego‑network topologies. Experiments across four LLM backbones, five graph structures, and 55 policy statements show that persuasion dynamics depend on topology, competition, topic, and model prior. The study finds that direct exposure predicts stance change, peer relays have measurable influence, and that post‑text analysis alone misses important movement, highlighting the need to evaluate multi‑agent persuasion through trajectory‑level processes, belief probes, exposure provenance, and action logs.
By Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit, Kung-Hsiang Huang, Saadia Gabriel, Chien-Sheng Wu, Nanyun Peng
arXiv:2606. 27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations.
By Utkarsh Sahu, Zhisheng Qi, Li Zhu, Yizhao Yang, Jun Li, Ryan Rossi, Yu Wang
The paper introduces a digital‑twin framework that simulates opinion dynamics in real Twitter networks by assigning agents attributes such as persona, emotions, centrality, stubbornness, and influence, and using Mistral‑7B to update opinions based on memory and social exposure. Validation on COVID‑19 and U.S. election 2020 datasets shows the framework reproduces opinion trajectories, reducing prediction error by over 50% compared to classical baselines, and improves structural alignment and polarization dynamics. Ablation studies reveal that agent attributes, memory, and social exposure all contribute to predictive fidelity, with agent attributes being the most critical.
By Omran Berjawi, Giuseppe Fenza, Rida Khatoun, Sherali Zeadally
The paper evaluates deep graph generative models against traditional network science models by comparing the topological similarity of generated networks to real-world networks and their effectiveness in identifying node immunization strategies for epidemic or misinformation spread. It finds that two deep graph generative models produce synthetic networks that closely resemble real-world structural properties, enabling them to identify effective immunization strategies.
By Tianrui Mao, Abele Malan, Megha Khosla, Lydia Chen, Huijuan Wang
The paper proposes a Friedkin‑Johnsen based framework to identify influential users in online social networks and assess how they shape community opinion. By manipulating initial opinions in experiments, the authors show that top influencers can significantly shift overall community sentiment, and their influence extends beyond direct neighbors to second‑degree contacts. The framework is validated on a tweet dataset from the U.S. presidential election, illustrating the power of digital influencers to alter public opinion.
By Omran Berjawi, Rida Khatoun, Giuseppe Fenza