There IS a problem: Misperceptions–eg election denialism, vax and climate skepticism–are common & impactful. But problem is less “Pope Endorses Trump” style fake news, more stories like these 👇 Not false, but misleading – and massively popular (see science.org/doi/10.1126/scie…)

New in TiCS w @DG_Rand @GordPennycook! It’s been ~10yrs since misinfo research exploded, but our paradigms are stuck in the post-2016 “fake news” model Time for new approaches: o True/False → Content that misleads o Belief → Behavior o Eval interventions in ambiguous settings

Super interesting analysis, especially the problematic role of domestic media amplifying the propaganda. We ran an analysis of Tenet Media and came to a similar conclusion: mediabiasdetector.seas.upenn…

Second, journalists should be vigilant, and avoid blindly repeating politicians' false claims or writing misleading clickbait headlines – like the examples attached 6/N

Bonus tweet -- in funny bit of timing, this paper comes out the same day as my graduation! (Pic feat. my very offline parents)

BUT, when we weight each headline’s persuasive effect by its number of views, we see a very different picture. The small fraction of unflagged stories that were almost as negatively persuasive as flagged misinfo were seen *vastly* more times than the flagged misinfo

The resulting distribution of predicted treatment effects shows that flagged misinfo *was* sig more harmful than unflagged content when people saw it, just like in our experiments

Using a novel methodology combining crowdsourcing and NLP, we generalized our exp results to predict causal effects on vax intent for all 13K highly viewed headlines from FB. Crowds effectively identified the most persuasive headlines, and NLP scaled the crowdsourcing results.

This single URL from the Chicago Tribune was seen by 55 million ppl – 6X the total of flagged misinfo It was factually accurate but - given tenuous link b/t vax & death, & the millions of people who did not suffer serious side effects – misleadingly implied vax was dangerous

People mostly saw links from mainstream/reputable sites– but some of this content was misleading+hesitancy inducing Mainstream stories covering rare deaths following vaccination attracted *massive* viewership on FB during the initial vaccine rollout, and weren’t flagged by FB

So misinfo was persuasive – but did people see it? Not so much! During Q1 2021, URLs flagged by FB’s fact-checking program received ~9 million views -- just 0.3% of vax-related views. Similarly, links to low-quality news sites accounted for just 5.1% of views

Our randomized survey exps assessed 130 headlines' causal effect on vax intentions Fact-checked misinfo *did* lower vax intentions by ~1.5pp, sig more than accurate content - BUT the best predictor of persuasion was whether the headline implied the vax was harmful – not falsity

New in @ScienceMagazine What FB news drove COVID vax hesitancy in US? False misinfo? Not so much: We find unflagged ‘vax-skeptical’ news had *46X larger* impact than flagged misinfo Why? Flagged misinfo had bigger impact when seen, but ~100x fewer views science.org/doi/10.1126/scie…

Ratings data were even more striking Ds rated 83% of notes by fellow Ds helpful, vs. 43% by Rs Rs rated 87% of notes by Rs helpful, vs. 26% by Ds 11/

Results were stark! Ds were 2X, and Rs were 3X, more likely to flag a tweet by a counterpartisan. Even though the majority of tweets (90%!) were classified misleading, bwatchers labeled many more co-partisan tweets as NOT misleading than counterpartisan ones 10/