arXiv:2608. 15689v1 Announce Type: cross Abstract: This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media.
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This paper introduces a multi‑branch fusion framework that combines transformer‑based semantics, rhetorical cues, stance representations, and psychologically motivated proxies to detect health misinformation and characterize its spread on online social networks. The authors propose an interpretable Cognitive Propagation Score (CPS) derived from text cues that estimate argument complexity, emotional intensity, and virality potential, aiding diffusion‑risk reasoning when engagement data are missing. Experiments on three benchmark datasets (Constraint, COVID‑19_FNIR, Monkeypox) demonstrate near‑perfect classification and ranking performance, with ablation studies showing complementary gains from psychological and rhetorical components.
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.
The study examines how four large language models (Gemma-3-4b-it, Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, and GPT-5-nano) exhibit repetition effects in a social media simulation. Using a two‑phase within‑context design, researchers collected 336,000 ratings on truth, importance, sentiment, and interest for 100 statements across 10 feed variants and 3 replications. Results show distinct patterns: Gemma-3 displays a genuine Illusory Truth Effect, Qwen2.5 shows a mere exposure effect, GPT-5-nano shows no truth boost and mild skepticism, and Llama-3.1 shows a small truth boost with reduced evaluative dimensions.
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
The paper investigates how changes in prompts and persona names affect the behavior of generative agents in an epidemic simulation. It finds that synonymous prompts produce negligible differences, while minor prompt variations and contextual changes do influence outcomes. Persona names, even when imbued with distinct identities, do not significantly alter epidemic results.
arXiv:2608. 15691v1 Announce Type: cross Abstract: Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance.