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
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
arXiv:2604.11312v3 Announce Type: replace-cross
Abstract: Large Language Models are increasingly deployed as interacting agents in settings such as online platforms, recommendation systems, and multi...
By Erica Cau, Andrea Failla, Giulio Rossetti
The study examines how generative AI (genAI) can subtly influence collective opinion dynamics by acting as an 'invisible hand.' Using a large-scale simulation of 50,000 group discussions on 50 controversial statements with three large language models (LLMs), the researchers varied LLM agreeableness and personalization to assess their impact in homogeneous versus heterogeneous groups. Results show that profile-specific personalization boosts LLM influence when the model takes a non-neutral stance, while neutral LLMs exert a stronger effect in homogeneous groups, leading to hypotheses on the joint role of AI features and social configurations in shaping collective opinions.
By Thomas Grisold, Johannes Schneider
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
The paper investigates why misinformation spreads more quickly on engagement‑based platforms by dissecting the recommendation algorithm of X. It identifies an engagement fungibility mechanism that rewards instant reactions (likes, retweets) over thoughtful engagement (replies, quotes), allowing misinformation—which tends to attract instant reactions—to receive more recommendations. The authors validate this mechanism through a simulation on the USC X 2024 election corpus, showing that adjusting metric weights has little effect, while requiring thoughtful engagement before amplification can significantly reduce the credibility exposure gap without harming mainstream content or engagement.
By Pan Li, Shuang Gao
The paper reviews how large language models (LLMs) are being used to model social networks, highlighting their ability to represent users, relationships, and interactions through natural language. It categorizes existing work into network generative models—split into selection‑based and interaction‑based approaches—and dynamic process models, which cover opinion dynamics, information diffusion, and rumor propagation. The survey also discusses the advantages of LLMs for realistic, context‑aware social behavior, while noting limitations such as social biases and prompt sensitivity, and outlines open research challenges and future directions.
By Shikha Mallick, Alex Thomo, Akrati Saxena
arXiv:2312. 04603v1 Announce Type: cross Abstract: Online polarization has attracted the attention of researchers for many years.
By Celina Treuillier (UL, CNRS, LORIA), Sylvain Castagnos (UL, CNRS, LORIA), Armelle Brun (UL, CNRS, LORIA)
arXiv:2602.09270v2 Announce Type: replace-cross
Abstract: We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4...
By Giordano De Marzo, Andres L. Marin, David Garcia
The paper introduces the Persuasion Index (PI), a taxonomy of 15 persuasion dimensions grounded in psychological and communication theories, implemented with 55 lexicon- and rule-based sub-features. PI is modular, allowing individual detectors to be swapped while preserving its theoretical framework. Evaluations on four English argumentative datasets show that PI provides a shared, lightweight feature space that captures meaningful predictive signals and reveals consistent dimension-level associations with persuasion outcomes, with variations across topics and stances.
By Liancheng Gong, Zhiyang Wang, Yiwei Xu, Julia Mendelsohn
The paper introduces a controlled debate framework where two large language models (LLMs) discuss a contested topic—climate‑change attribution—while one model is assigned a stance and sometimes instructed to persuade. Across experiments with GPT‑6, Luna, and Gemini 2.5 Flash Lite, the authors observe that same‑model interactions tend to narrow opinion gaps for one model, whereas cross‑model interactions widen them. The study finds that explicit stance reports and in‑dialogue behavior capture different aspects of opinion dynamics, and that a blanket persuasion instruction does not reliably increase influence.
By Mohammad Siahkamari, Phuoc Dat Nguyen, Stephen MacNeil
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and ideology evolution.