🚨Out now in Nature!🚨
A fundamentally new way of fighting misinfo online:

Surveys+field exp w >5k Twitter users show that gently nudging users to think about accuracy increases quality of news shared- bc most users dont share misinfo on purpose

1/ ImageImage
Why do people share misinfo? Are they just confused and can't tell whats true?

Probably not!

When asked about accuracy of news, subjects rated true posts much higher than false. But when asked if theyd *share* online, veracity had little impact-instead was mostly about politics Image
So why this disconnect between accuracy judgments and sharing intentions? Is it that we are in a "post-truth world" and people no longer *care* much about accuracy?

Probably not!

Participants overwhelmingly say that accuracy is very important when deciding what to share Image
We argue the answer is *inattention*: accuracy motives are often overshadowed bc social media focuses attention on other factors, eg desire to attract/please followers

This lines up w past finding that more intuitive Twitter users share lower quality news
We test these competing views by shifting attention towards accuracy in 4 exps (total N=3485) w MTurkers & ~representative sample. If people don’t care much about accuracy, this should have no effect. But if problem is inattention, this should make sharing more discerning.
In one exp, Treatment participants rate accuracy of every news post before indicating how likely they'd be to share it. In Control they just indicate sharing intentions

Treatment reduces sharing of false news by 50%! Most of remaining sharing of false news explained by confusion Image
How about a light-weight prompt?

Treatment=subjects rate accuracy of 1 nonpolitical headline at start of study, subtly priming concept of accuracy

Significantly increases quality of subsequent sharing intentions (reduces sharing of false but not true news) relative to control Image
Next, we test our intervention "in the wild" on Twitter. We build up follower-base of users who retweet Breitbart or Infowars. We then send N=5379 users a DM asking them to judge the accuracy of a nonpolitical headline (w DM date randomly assigned to allow causal inference) Image
We quantify quality of news tweeted using fact-checker trust ratings of 60 news sites (pnas.org/content/116/7/…)- at baseline, our users share links to quite low-quality sites

We assess intervention by comparing links in 24 hrs after receiving DM to links from users not yet DMed Image
We find increase in quality of news retweeted after receiving accuracy-prompt DM! 4.8% increase in avg quality, 9.0% increase in summed quality, 3x increase in discernment. Fraction of RTs to DailyCaller/Breitbart 🡳, to NYTimes 🡱

Sig effect in >80% of 192 model specifications Image
Agent-based simulations show how this positive impact can be amplified by network effects. If I dont RT, my followers dont see it and wont RT, so none of their followers will see it etc. Plus, effect sizes observed in our exp could certainly be increased through optimization Image
We also formalize our inattention account using utility theory. Due to attention constraints, agents can only attend to a subset of terms in their utility fn. So even if you have a strong pref for accuracy, accuracy wont impact sharing choice when attention is directed elsewhere! Image

Fitting model to the experimental data shows avg participant cares about accuracy as much or more than partisanship (confirming survey results)- but attention is often directed away from accuracy

Plus, treatment specifically reduces sharing of more implausible news Image
These studies help us see past the illusion that everyday citizens on the other side must be either stupid or evil- instead, we are often simply distracted from accuracy when online. Another implication of our results is that widely-RTed claims are not necessarily widely BELIEVED
Our treatment could be easily implemented by platforms, eg periodically asking users to rate the accuracy of random posts. This primes accuracy (+generates useful crowd ratings to identify misinformation )

Scalable+doesnt make platforms arbiters of truth!
Here we focused on political news, but in follow-up studies we showed that the results generalize to COVID-19 misinformation as well (eg in this paper that we frantically pulled together in the first few days of the pandemic)
We hope that tech companies will investigate how they can leverage accuracy prompts to improve the quality of the news people share online

To that end, we're really excited about an ongoing collaboration we have with researchers at @Google's @Jigsaw -see psyarxiv.com/sjfbn
We were also really excited to see @tiktok_us, in collaboration with @IrrationalLabs, develop assess and implement an intervention based in part on our accuracy-prompt work

Hoping that @jack @Twitter @Facebook and others will be similarly interested

This study is the latest in our research group's efforts to understand why people believe and share misinformation, and what can be done to combat it. For a full list of our papers, with links to PDFs and tweet threads, see docs.google.com/document/d/1k2…
Finally, if you made it this far into the thread and want to know how this work connects to broader psychological and cognitive science theory, check out this recent review "The Psychology of Fake News" that @GordPennycook and I published in @TrendsCognSci authors.elsevier.com/sd/article/S13…
I'm extremely excited about this project, which took years & was led by @GordPennycook @_ziv_e @MohsenMosleh w invaluable input from coauthors @AaArechar @deaneckles

Please let us know your comments, critiques, suggestions etc. Thanks!!

Ungated PDF: psyarxiv.com/3n9u8
I also wanted to share this @sciam piece that @GordPennycook and I wrote summarizing the paper and related work scientificamerican.com/article/most-p…

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More from @DG_Rand

1 Nov 20
New WP for your doomscroll:

➤We follow 842 Twitter users with Dem or Rep bot
➤We find large causal effect of shared partisanship on tie formation: Users ~3x more likely to follow-back a co-partisan


Led by @_mohsen_m w/ @Cameron_Martel_ @deaneckles

We are more likely to be friends with co-partisans offline & online

But this doesn't show *causal* effect of shared partisanship on tie formation
* Party correlated w many factors that influence tie formation
* Could just be preferential exposure (eg via friend rec algorithm)
So we test causal effect using Twitter field exp

Created bot accounts that strongly or weakly identified as Dem or Rep supporters

Randomly assigned 842 users to be followed by one of our accounts, and examined the prob that they reciprocated and followed our account back

Read 8 tweets
8 Oct 20
🚨Working paper alert!🚨
"Scaling up fact-checking using the wisdom of crowds"

We find that 10 laypeople rating just headlines match performance of professional fact-checkers researching full articles- using set of URLs flagged by internal FB algorithm

psyarxiv.com/9qdza/ Image
Fact-checking could help fight misinformation online:

➤ Platforms can downrank flagged content so that fewer users see it

➤ Corrections can reduce false beliefs (forget backfires: e.g. link.springer.com/article/10.100… by @thomasjwood @EthanVPorter)

🚨But there is a BIG problem!🚨
Professional fact-checking doesnt SCALE

eg last Jan, FB's US partners factchecked just 200 articles/month!
Even if ML expands factcheck reach theres desperate need for scalability

FCs also perceived as having liberal bias which creates political issues
Read 14 tweets
24 Mar 20
Today @GordPennycook & I wrote a @nytimes op ed

"The Right Way to Fix Fake News"

tl;dr: Platforms must rigorously TEST interventions, b/c intuitions about what will work are often wrong

In this thread I unpack the many studies behind our op ed

Platforms are under pressure to do something about misinformation. Would be simple to rapidly implement interventions that sound like they would be effective.

But just because an intervention sounds reasonable doesn’t mean that it will actually work: Psychology is complex!

For example, its intuitive that emphasizing headline's publisher (ie source) should help people tell true vs false Low quality publisher? Question the headline.

But in a series of experiments, we found publisher info to be ineffective!


Read 14 tweets
17 Mar 20
🚨New working paper!🚨

"Fighting COVID-19 misinformation on social media:
Experimental evidence for a scalable accuracy nudge intervention"

We test if an intervention we developed for political fake news works for #COVID19- seems like YES!

PDF: psyarxiv.com/uhbk9/

Previously we found people share political misinfo b/c social media distracts them from accuracy- NOT b/c they cant tell true v false, NOT b/c they dont care about accuracy

So nudging them to think about accuracy improved quality of news they shared!

Like everyone else we're losing sleep over #COVID19

To try to feel (slightly) useful, we decided to see how similar COVID-19 misinfo was to political misinfo from a cog psych perspective- and if the accuracy nudge we'd come up with might help fight COVID-19 misinfo online

Read 15 tweets
17 Nov 19
🚨Working paper alert!🚨 "Understanding and reducing the spread of misinformation online"

We introduce a behavioral intervention (accuracy salience) & show in surveys+field exp w >5k Twitter users that it increases quality of news sharing


We first ask why people share misinformation. It is because they simply can't assess the accuracy of information?

Probably not!

When asked about accuracy, MTurkers rate true headlines much higher than false. But when asked if theyd share online, veracity has little impact
So why this disconnect between accuracy judgments and sharing intentions? Is it that we are in a "post-truth world" and people no longer *care* much about accuracy?

Probably not!

Those same Turkers overwhelmingly say that its important to only share accurate information.
Read 11 tweets

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