Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel simulation paradigm employing autonomous LLM-based agents to replicate human behavioral dynamics—to explore the predictors and mechanisms underlying stance change in a controlled, fully observable environment. We simulate a social media with 1,000 LLM-driven agents, equally split between Democratic and Republican profiles, engaged in discussions about the 2020 US election. Our analysis reveals that agents who change political orientation exhibit lower activity levels, reduced network centrality, and more polarized emotional expression compared to those maintaining consistent positions. Self-reported motivations cluster into four categories: desire for constructive conversation (47.9%), internal factors (20.8%), fact-checking influence (16.7%), and previous interactions (14.6%). While we do not claim agents replicate humans’ stance change behavior, the emergent patterns observed in our simulation qualitatively align with established findings from empirical research, suggesting that GABM may capture meaningful dynamics of opinion change.
Valerio La Gatta, Gian Marco Orlando, Marco Perillo et al.· Proceedings of the 37th ACM...· 0 citations
The proliferation of Large Language Models (LLMs) has significantly lowered the barriers to generating sophisticated misinformation, posing unprecedented challenges to fake news detection systems. Although generative agents have shown promise in constructive applications like fact-checking workflows, their potential for collaborative fake news generation remains unexplored. This paper introduces the first multi-agent framework for fake news generation, where specialized LLM-powered agents collaboratively transform authentic news articles into falsified yet plausible versions. Our pipeline employs five specialized agents: a Semantic Analyzer that extracts information-dense components, a Salient Sentence Editor and Number Modifier that manipulate key textual and numerical elements, a Narrative Modifier that ensures overall coherence, and a Title Editor that generates contextually appropriate headlines. We evaluate this system on two benchmark datasets, the ISOT Fake News Dataset and the All The News Dataset, against six state-of-the-art detection models. Our results demonstrate that the proposed framework achieves deception success rates exceeding 90% while maintaining high semantic similarity to original articles. Notably, even smaller generative models can surpass 80% success against high-capacity detectors, highlighting critical vulnerabilities in current fake news detection systems and underscoring the urgent need for more robust defense mechanisms against coordinated generative AI attacks.
Gian Marco Orlando, Diego Russo, Valerio La Gatta et al.· ACM Transactions on Intellig...· 0 citations
Named Entity Recognition (NER) in specialized domains like biomedicine suffers from acute data scarcity, requiring expensive expert annotations. While data augmentation offers a promising solution, it inevitably introduces noisy and mislabeled samples that can degrade model performance. This problem is amplified in few-shot scenarios where every training example matters. We introduce PALAUNER (Policy-based Active Learning to Augment Named Entity Recognition), a reinforcement learning framework that learns to select high-quality samples from augmented data pools. Using a deep Q-network, our agent evaluates samples based on content features and model predictions, deciding which examples will improve NER performance. Experiments across five BioNER benchmarks demonstrate that PALAUNER consistently enhances diverse augmentation methods, from simple perturbations to GPT-based generation. Average F1 improvements are of 0.5−7.1 points in few-shot settings. PALAUNER’s modular design enables seamless integration with emerging augmentation techniques, providing a generalizable solution for training data quality enhancement. We publicly release our code on GitHub: (https://github.com/picuslab/palauner).
M. Postiglione, Andrea Vignali, Giancarlo Sperlí et al.· Data mining and knowledge di...· 0 citations
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