Multimodal Adaptive Graph-based Intelligent Classification Model (MAGIC) for Fake News Detection Using Text and Images
This research proposes a novel multimodal model called MAGIC that leverages graph neural networks and adaptive learning to effectively detect fake news by integrating textual and visual features from social media posts.
The Impact of Fake News on Social Media Users of Different Age Groups and the Potential of AI and ML for Mitigation
This research paper explores the escalating issue of fake news on social media, its disproportionate impact on different age groups, and the potential of AI and ML in combating its spread.
FNDEX: A System for Detecting Fake News and Doxxing Using Explainable AI and Anonymization Techniques
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.
Hierarchical Detection of Human- and Machine-Authored Fake News in Urdu
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.
The Effectiveness of Humans and LLMs in Detecting AI-Generated Fake News: Insights from a University-Level Competition
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.
NexusIndex: A Novel Framework for Fake News Detection Using Multi-Model Embeddings and Advanced Vector Indexing
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.
Evaluating Large Language Models for Detecting Real-Time Fake News: An Adversarial Approach
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large language models (LLMs) struggle to detect real-time fake news effectively, relying on superficial patterns in outdated datasets rather than factual reasoning.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.
異なるネットワーク構造下における、大規模言語モデル駆動型マルチエージェントシミュレーションを用いたニュース拡散に関する研究
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。
A Multimodal Multilingual Approach to Detecting Fake News in Low-Resource Indic Languages
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: A Novel Approach to Fake News Detection Using Web Scraping and Natural Language Inference
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.
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