DAWN: Enhancing Fake News Detection by Leveraging Engagement Earliness in a Temporality-Aware Setting
Social media engagement patterns, particularly the earliness of engagement, can be a strong indicator of news veracity and can be leveraged to improve fake news detection models, especially in real-world scenarios where temporal information is crucial.
Leveraging Regularized LSTM Networks for Enhanced Fake News Detection
This research paper presents the development and evaluation of three increasingly sophisticated machine learning models, culminating in an optimized deep learning model achieving 98% accuracy in detecting fake news articles.
Detecting Fake News from Unseen Domains Using Causal Subgraphs in Propagation Networks
Extracting and analyzing causal substructures within news propagation networks can effectively detect fake news, even in unseen domains, by mitigating domain-specific biases prevalent in traditional models.
Comments-Assisted Early Fake News Detection: Leveraging Historical Data to Enhance Accuracy and Timeliness
This research proposes a novel method, CAS-FEND, to overcome the accuracy-timeliness dilemma in fake news detection by leveraging historical user comments to train a content-only model capable of early detection of fake news with high accuracy.
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.
