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.
By Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao
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 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.
By Azza Bouleimen, Nicol\`o Pagan, Anik\'o Hann\'ak
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
By Petra Ferenz, Ava Keeling, Tobias O'Keefe, Lorenzo Stigliano, Francesco Di Lauro, Andres Colubri, Jasmina Panovska-Griffiths
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.
By Ross Williams, Niyousha Hosseinichimeh
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.
By Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao
The paper introduces a signaling‑game framework to model how individuals strategically misreport behavioral data—such as mask usage and vaccination status—to public health authorities. It provides a generative model of such adversarial data and a method for authorities to recover reliable signals, analyzing equilibrium outcomes and evaluating how deception affects epidemic control. Large‑scale simulations and real‑world validation show that well‑designed sender and receiver strategies can still maintain effective epidemic control even with pervasive dishonesty, and that behavioral distortions often follow structured patterns rather than random noise.
By Yiqi Su, Christo Kurisummoottil Thomas, Walid Saad, Sanmay Das, Bud Mishra, Naren Ramakrishnan
arXiv:2606. 08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them.
By Rahul Gorijavolu, Kaushik Madapati, Pritika Vig, Rawan Abulibdeh, Nikhil Jaiswal, Mahri Kadyrova, Zeamanuel Hailu Tesfaye, Charles Senteio, Paula Maurutto, Leo Anthony Celi
arXiv:2608. 16323v1 Announce Type: cross Abstract: The spread of misinformation on social networks poses a significant challenge to online communities and society at large.
By Enrico Verdolotti, Luca Luceri, Silvia Giordano
arXiv:2607. 12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation.
By Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain
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 study compares communication styles of large language models (LLMs) and humans in explaining COVID‑19 misinformation, using a dataset of 1,498 fact‑checking claims and 99 blinded reader evaluations. LLM‑generated explanations scored lower on persuasive strategies, certainty, and alignment with social values, yet over 60% of participants preferred LLM content for clarity, completeness, and persuasiveness. The findings suggest that reader preference may not align with traditional measures of communication quality, highlighting both the promise and limits of LLMs in health communication.
By Jiawei Zhou, Kritika Venkatachalam, Minje Choi, Koustuv Saha, Munmun De Choudhury