← Back to all news
arXiv Machine Learning By Parth Bramhecha, Smit Deshmukh, Sairaj Bodhale, Adwait Borate, Raviraj Joshi

BharatGather: A Culturally-Informed Benchmark Dataset for Misinformation and Fake News Detection in Indian Public Events

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computation and Language
Sep 18

FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

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.

By Giovanni Spitale, Federico Germani
llms
More like this →
arXiv Machine Learning
Sep 25

Agentic Detection of Online Conspiracies

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.

By Lior Biton, Oren Tsur
agentssafety
More like this →
arXiv Computation and Language
6d ago

TRIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection

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.

By Shifali Agrahari, Sujit Kumar, Sanasam Ranbir Singh
llmsbenchmarkssafety
More like this →