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Reposted by Fangjing Tu

Ross Dahlke @rossdahlke.bsky.social · 03/04/2026

Most web browsing studies analyzing news and misinformation operate at the domain level. Work by me, @fangjingtu.bsky.social et al., scrapes the content from web visits to go beyond the source to the content level, finding significant topical and linguistic variation doi.org/10.1145/3757571

Screenshot of a paper titled “Contextualizing Misinformation: A User-Centric Approach to Linguistic and Topical Patterns in News Consumption,” authored by Ross Dahlke and colleagues. The abstract says the study uses web-browsing data from 1,240 U.S. adults during the 2020 election to compare misinformation and hard news. It finds that misinformation people consumed was generally easier to read, more negative in tone, and more morally framed, with substantial variation across topics and across groups such as older adults and Republicans.

Image description
Two side-by-side horizontal bar charts compare topic distributions for hard news and misinformation. Hard news is led by general news at 35.9%, followed by U.S. electoral politics at 27.0%, social issues at 17.1%, COVID-19 at 14.2%, and health at 5.7%. Misinformation is much more concentrated in U.S. electoral politics at 53.0%, followed by social issues at 23.7%, COVID-19 at 11.6%, general news at 6.8%, and health at 4.9%.

Two stacked line charts show how topic shares changed over time from late August to early December 2020, with a vertical marker at Election Day 2020. In misinformation, U.S. electoral politics rises sharply in October and November and becomes the dominant topic around the election. In hard news, general news remains largest throughout, while U.S. electoral politics also spikes around Election Day before declining afterward.

Image description
Two stacked line charts show how topic shares changed over time from late August to early December 2020, with a vertical marker at Election Day 2020. In misinformation, U.S. electoral politics rises sharply in October and November and becomes the dominant topic around the election. In hard news, general news remains largest throughout, while U.S. electoral politics also spikes around Election Day before declining afterward.

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J. Nathan Matias @natematias.bsky.social · 15/03/2026

A reminder that research to detect and study disinformation is vitally important for informed societies. www.nytimes.com/interactive/...

nytimes.com

Cascade of A.I. Fakes About War With Iran Causes Chaos Online (Gift Article)

The technology has been used to create misleading fakes before. But never at this scale.

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Yiqing Xu @yiqingxu.bsky.social · 18/02/2026

1/ Sorry for double-posting from X. Sharing a new working paper for the Year of the Horce 🐎: "An AI-assisted workflow that scales reproducibility in empirical research" (bit.ly/repro-ai) w/ Leo Yang Yang

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J. Nathan Matias @natematias.bsky.social · 18/12/2025

Should scientists apply to OpenAI's fund for research on AI & mental health? Should policymakers consider it a credible safety effort? Avriel Epps & I see it as "grantwashing," and it's an insult to anyone whose loved one's death involved chatbots. We explain: www.techpolicy.press/beware-of-op...

techpolicy.press

Beware of OpenAI's 'Grantwashing' on AI Harms | TechPolicy.Press

J. Nathan Matias and Avriel Epps say OpenAI's announced research funding is the perfect corporate action to make sure we don't find answers for years.

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Reposted by Fangjing Tu

Harry Yan @harryyan.bsky.social · 18/04/2025

In collaboration with @ryanmoore.bsky.social @fangjingtu.bsky.social and Dr. Jeff Hacock, and supported by Stanford Social Media Lab, and @stanfordcyber.bsky.social.

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