This research paper introduces FNDEX, a novel system that leverages transformer models to detect fake news and doxxing, employs a three-step anonymization process to protect sensitive information, and utilizes explainable AI (XAI) to provide transparency and accountability for its outcomes.
This research paper introduces a novel hierarchical approach to detecting fake news in Urdu, addressing the challenge of identifying both human-written and machine-generated misinformation.
While Large Language Models (LLMs) excel at identifying real news, both humans and LLMs struggle to detect AI-generated fake news, particularly when creators employ diverse prompting and optimization strategies in collaboration with AI.
The NexusIndex framework leverages the power of multi-model embeddings and advanced vector indexing techniques, specifically integrating a FAISS layer within a neural network, to significantly improve the accuracy and efficiency of fake news detection.
Current large language models (LLMs) struggle to detect real-time fake news effectively, relying on superficial patterns in outdated datasets rather than factual reasoning. This paper proposes an adversarial approach to generate more challenging fake news, revealing the need for improved LLM-based detection methods.
本稿では、大規模言語モデルを用いたマルチエージェントシミュレーションが、異なるネットワーク構造における偽情報拡散のメカニズムを解明する有効な手段であることを示し、効果的な対策にはネットワーク構造に応じた個別対応が必要であることを強調する。
This research introduces a new dataset and framework for detecting fake news in low-resource Indic languages, using a multimodal approach that combines textual and visual analysis to improve accuracy and address the unique challenges of misinformation in these languages.
VERITAS-NLI leverages web scraping and natural language inference to dynamically verify news headlines against real-time information, achieving higher accuracy than traditional machine learning and BERT models.
The analysis of user interaction patterns and sentiment dynamics within Reddit comment threads reveals distinct differences between fake and real news discussions, offering potential avenues for early fake news detection.
Integrating social context features with news content features enhances the accuracy of fake news detection in under-resourced languages, particularly Amharic.