Abstract This study investigated the emergence of generative artificial intelligence as a vector for misinformation during heightened US-Iran geopolitical tensions, drawing on a systematic analysis of The New York Times investigative report (March 2026). We analyzed 110+ verified... This study investigated the emergence of generative artificial intelligence as a vector for misinformation during heightened US-Iran geopolitical tensions, drawing on a systematic analysis of The New York Times investigative report (March 2026). We analyzed 110+ verified AI-generated visual media items identified within the initial fourteen-day escalation period, employing a multi-layered verification framework encompassing visual forensics, digital watermark analysis, algorithmic detection, and cross-referencing with authoritative sources. Results reveal pronounced narrative asymmetry, with approximately 78% of content advancing a pro-nation strategic framing across five thematic categories: civilian targeting, military fabrications, infrastructure damage, symbolic propaganda, and event re-enactments. Aggregate viewership exceeded several million impressions across public platforms, with propagation dynamics aligning with epidemic diffusion models characterized by high infection rates and low recovery rates. Comparative forensic analysis identified consistent differentiators between synthetic and authentic footage, including cinematic exaggeration, symbolic artefacts, and physical inconsistencies in AI-generated content. Evaluation of mitigation strategies indicates that cross-verification with trusted sources remains the most reliable, while platform-level interventions have proven heterogeneous and largely reactive. Modeling using SIR, SEIR, and rumor-propagation frameworks quantifies the viral potential of emotionally charged conflict visuals and the impact of network structure on dissemination. These findings establish generative AI as an operationalized force multiplier in cognitive warfare, underscoring urgent needs for hybrid detection approaches, proactive platform governance, and coordinated policy responses to preserve informational integrity in algorithmically mediated conflict environments. Read More Read Less

Abstract The proliferation of misinformation on social media poses significant societal risks, necessitating robust analytical frameworks. This study presents a comprehensive analysis of misinformation clusters and virality dynamics within Reddit communities using topic modeling and epidemiological diffusion models.... The proliferation of misinformation on social media poses significant societal risks, necessitating robust analytical frameworks. This study presents a comprehensive analysis of misinformation clusters and virality dynamics within Reddit communities using topic modeling and epidemiological diffusion models. Utilizing a longitudinal dataset sourced from the Zenodo repository spanning 2012 to 2024, we employed TF-IDF vectorization and K-means clustering to identify five dominant thematic groups, including Health, US Politics, and General Discussions. Misinformation detection was enhanced with a fine-tuned DistilBERT model, achieving 94-96% accuracy and surpassing traditional logistic regression baselines. Results indicate that while General Discussions contain the highest absolute volume of misinformation, US Politics exhibits the highest density, creating critical risk zones. Virality metrics reveal a credibility paradox: misleading content achieves higher upvote ratios than factual news, suggesting stronger audience resonance despite lower overall reach. Furthermore, an adapted SEIR (Susceptible-Exposed-Infected-Recovered) framework confirms that information spread follows epidemic-like patterns, with political topics acting as infectious nodes and general topics providing susceptible populations until saturation. Explainable AI techniques, specifically LIME, validated model interpretability by highlighting linguistic markers such as "fake" and "hiding." These findings underscore that misinformation is not randomly distributed but concentrated in specific thematic clusters where high virality overlaps with high density. Strategic monitoring prioritizing these high-risk clusters is essential for mitigating information epidemics and developing effective countermeasures against the evolving landscape of online disinformation. Future research should incorporate temporal dynamics into the model to predict viral spikes before saturation occurs. Read More Read Less

Abstract This study investigates the impact of oil price shocks on global economic vulnerability during the hypothetical 2026 US-Iran conflict, specifically focusing on the closure of the Strait of Hormuz. Integrating time-series decomposition, machine learning, and diffusion-based modeling,... This study investigates the impact of oil price shocks on global economic vulnerability during the hypothetical 2026 US-Iran conflict, specifically focusing on the closure of the Strait of Hormuz. Integrating time-series decomposition, machine learning, and diffusion-based modeling, the research analyzes crude oil price dynamics and their macroeconomic consequences. The methodology employs seasonal trend decomposition, change-point detection, and an adapted robust SEIR framework to track shock propagation across nations. Additionally, hybrid machine learning models utilizing Gradient Boosting and Random Forest algorithms forecast price spikes and volatility. Results identify significant structural breaks on March 4 and March 9, 2026, coinciding with conflict escalation and driving Brent crude to $104 per barrel. The SEIR model categorizes economies into Susceptible, Exposed, Infected, and Resilient states, revealing high vulnerability in South Asian nations like Pakistan and Bangladesh due to import dependence. Scenario analysis projects GDP contractions ranging from -1.62% in the best-case de-escalation to -28.80% for Iran under prolonged conflict. Crucially, findings indicate that early policy interventions can reduce economic damage by 30-40%. The study concludes that combining diffusion models with machine learning enhances forecasting accuracy under geopolitical uncertainty. It emphasizes that shock severity depends not only on market dynamics but also on policy responsiveness, urging coordinated strategic reserve releases and rapid diplomatic action to mitigate systemic risks in energy-dependent economies. Future research should incorporate longer historical datasets to further refine predictive capabilities Read More Read Less