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This paper presents a multimodal NLP framework that fuses XLM-RoBERTa and CLIP with geospatial and sarcasm features to detect fake news and predict violence-driven mob activity, achieving 98% test accuracy on a 138,256-sample Bangla/English dataset.
An article discussing the ironic situation where AI-generated fake news complains about the threat of AI fake news to real journalism.
Researchers from Kennesaw State University investigate cross-prompt generalization in detecting AI-generated fake news using interpretable linguistic features (lexical diversity, readability, emotion). A random forest classifier trained on one prompting strategy and tested on another achieves AUC values of 0.988–1.000, suggesting these features capture stable, generalizable properties of AI-generated text.
Proposes the CORE framework that endows multimodal large language models with explicit conflict-capturing capability for generalizable manipulation detection, adapting to unseen manipulation types with few or zero samples.
This paper introduces BOUTEF, a large-scale multilingual corpus for studying fake news in Algeria and Tunisia, covering Arabic dialects, Arabizi, French, English, and code-switching. It includes empirical analysis of linguistic strategies and engagement dynamics.
A correction issued by the Department of War clarifies that SpaceX remains a strong and valued partner, refuting false claims in a news article.
A rant about the proliferation of fake references and AI-hallucinated data in tech articles, using examples of a false story about Swedish crows and an inflated claim about code review defect detection.
Donald Trump stated that Israel did not urge him to attack Iran; his decision was driven by the Oct 7, 2023 events and his belief that Iran must not obtain nuclear weapons, while also criticizing media and polls as largely fabricated.