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.
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 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 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.
The paper presents a large‑scale study of ragebait on Japanese X, developing an ensemble classifier trained on a dataset labeled with the help of a large language model. The detector was applied to a vast collection of Japanese posts, revealing that ragebait is especially common in politically and socially contentious topics such as politics, discrimination, public health, and interpersonal conflict. Ragebait posts spread more quickly and elicit stronger negative emotions—anger, fear, disgust, sadness, and surprise—than non‑ragebait posts.
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
The paper presents the Temporal Coherence Score (TCS), a continuous, interpretable metric for detecting temporal inconsistencies in political news. TCS is computed through a four‑stage pipeline that extracts temporal facts, builds a temporal knowledge graph, verifies consistency using internal rules and external references, and aggregates scores with explanations. On a benchmark of 100 political articles with injected errors, TCS achieves 0.909 precision, providing detailed explanations for each flagged inconsistency.
arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.
The study investigates how large language models (LLMs) handle diverse Indian oral traditions, using the Rajasthani Pabuji epic, Tamil Sangam poetry, and Bengali folk tales as case studies. By prompting Claude Sonnet and Gemini with 54 generation requests across generic, culturally specific, and regional-language prompts, the authors measured reference drift and cross-tradition convergence using Sentence‑BERT embeddings. Results show that while outputs stay closer to their own tradition than to others, there is significant cross‑tradition similarity (0.52–0.66), indicating partial homogenisation; moreover, regional‑language prompting consistently reduced fidelity to authentic traditions.
The paper introduces SWARM, a multilingual dataset of 2,183 search engine results in nine languages, annotated for support of Russian propaganda narratives. It evaluates a source-based blocklist, supervised classifiers, and zero‑shot large language models, finding that blocklists miss most propaganda and that content‑level models vary in performance, with the best LLM achieving an F1 of 0.73. The study highlights the need for per‑language, content‑level detection of search‑borne propaganda.
arXiv:2609.12678v1 Announce Type: new Abstract: Short-video platforms have become a primary news source for the public, which has also enabled the widespread dissemination of fake news videos. We stu...