BharatGather is a curated, multi-source dataset designed for binary misinformation classification in Indian public events such as religious festivals, political rallies, and cultural gatherings. The corpus contains 14,646 records assembled through systematic web scraping of fact‑checking platforms, multimedia transcript extraction, and LLM‑mediated synthetic augmentation to capture narrative diversity. It serves as a culturally informed benchmark to evaluate and develop fake‑news detection systems tailored to the socio‑cultural nuances of India’s mass‑gathering context.
arXiv:2607. 02734v1 Announce Type: cross Abstract: Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation.
arXiv:2609.20838v1 Announce Type: new Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-en...
FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
arXiv:2609.36850v1 Announce Type: new Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
The paper introduces SHAKTI, a large-scale Hindi benchmark for detecting AI-generated text, covering multiple genres, LLM families, prompting strategies, and adversarial transformations. It evaluates the benchmark with human annotation and examines linguistic, grammatical, and sentiment differences between human and AI texts. Building on these insights, the authors propose TRIDENT, a round‑trip translation‑based detection framework with three variants that outperforms existing baselines and demonstrates robustness across seven languages and adversarial settings.
The paper introduces BanglaSafe, a benchmark of 879 Bengali prompts that covers 17 culturally grounded harm categories and five prompting conditions. Evaluation of 18 frontier LLMs shows that 53.6% of responses are unsafe or partially unsafe, with 14.7% containing strictly harmful content. The study finds that the writing style within Bengali has a stronger impact on safety than the language switch itself, and that current safety classifiers struggle to reliably evaluate Bengali content.
arXiv:2512.20257v2 Announce Type: replace Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
The paper presents a theory-informed computational framework that converts cross-disciplinary theories of fake news into measurable features for automated detection and explanation. By reviewing theories from social sciences, psychology, economics, and more, the authors establish a broad theoretical foundation for computational modeling. Experiments on benchmark datasets demonstrate that theory-derived features are predictive, provide interpretable diagnostic signals, and that multi-feature models generally outperform individual features, though gains are modest.
arXiv:2610.33464v1 Announce Type: new Abstract: Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighti...
The paper presents a large‑scale study of ragebait—content designed to provoke anger—on Japanese posts on X. It introduces a labeled dataset created with a large language model, trains Japanese language models, and builds an ensemble classifier that detects ragebait. Applying this detector to a vast dataset reveals that ragebait is especially common in politically and socially contentious topics, spreads faster, and elicits stronger negative emotions than non‑ragebait posts.