Cameron Martel

Cameron Martel

@cameronmartel.bsky.social

Assistant Professor at Johns Hopkins Carey Business School. Studies misinformation & inauthentic behavior online.

444 followers278 following104 posts

Cameron Martel reposted

📣 Yale workshop, Oct 16-17! 📣 How could/should content ranking work? What's new in content moderation? How can platforms promote civility? Hosted by Yale's Institute for Foundations of Data Science (FDS). Great speakers! Submit posters by 9/22! Spread the word! yalefds.swoogo.com/socialalgori...

yalefds.swoogo.comNew Directions in Social Algorithms Research on October 16-17, 2025 at Yale UniversityAs social media algorithms increasingly mediate social experiences, there has been a rapid increase in research on the effects of how these algorithms are configured, alternatives to engagement-centri...

Cameron Martel reposted

🚨In PNAS🚨 The right often accuses fact-checkers of political bias But we analyzed Community Notes on Musk's X and found posts flagged as "misleading" are 2.3x more likely to be written by Reps than Dems! The issue is Reps sharing misinformation, not fact-checker bias... www.pnas.org/doi/10.1073/...

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Caveats: -Engagement ≠ belief updating (tho it’s an important first step) -Social corrections can have other negative effects (eg downstream lower quality reposting) dl.acm.org/doi/abs/10.1... -Hard to measure (presumably positive) third-party effects of social corrections on observers in field

dl.acm.orgPerverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Fi...
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A second survey exp found that minimal social connections foster a general norm of responding, such that ppl feel more obligated to respond - and think others expect them to respond more - to ppl who follow them, even outside the context of misinfo correction

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Exploratory analyses also show that in both survey & field exps, extreme partisanship moderates the effects of social connection on engagement - social connection increases engagement for co-partisans, but decreases engagement for politically extreme counter-partisans

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Users were also more likely to block our bots in the social counter-partisan condition (consistent w our @pnasnexus.org paper on greater blocking of counter-partisans). But this resulted in differential treatment delivery- we could not send corrections to users who blocked our bots shorturl.at/eG5bs

academic.oup.comBlocking of counter-partisan accounts drives political assortment on TwitterAbstract. There is strong political assortment of Americans on social media networks. This is typically attributed to preferential tie formation (i.e. homo

To account for this we (i) compare unaffected conditions (all but social counter-partisan) & (ii) perform principal stratification (weighting obs in unaffected conditions by p(success treat delivery) had they been in social counter-partisan condit)

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We sent corrections to 1,586 users & measured p(engage w correction): (i) Among users in the co-partisan condition, social connection had a sig positive effect on engagement (ii) Among users in the baseline (non-social) condition, no evidence of effect of shared partisanship on engagement

Maybe users simply did not notice or believe the partisanship manip? Prob not: we looked at the follow-back rates in the social condition, & partisanship had a strong effect (consistent w our @pnas.org paper on greater follow-back of copartisans) shorturl.at/B58Xh

pnas.orgPNASProceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
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We created human-looking bots & corrected users who shared debunked URLs We randomized whether our bots (i) were co-partisan or counter-partisan for the to-be-corrected user (ii) followed the user & liked some of their tweets before correcting them (creating a minimal social connection)

Cameron Martel reposted

Cameron Martel reposted

Nice blog post by @cameronmartel.bsky.social on our NHB paper showing fact-checker warnings work even for people who distrust fact-checkers. Particularly relevant re Meta's rollback of fact checking based (among other things) on claim that fact-checkers lost publics trust spsp.org/news/charact...

spsp.orgFact-checker Warnings Are Surprisingly Effective Even For Skeptics | SPSPEven when people distrust fact-checkers, they’re still influenced by warning labels on false news.

Cameron Martel reposted

New WP! The illusory truth effect (repetition -> belief) is core to psych of beliefs, & thought to be a deep bias impacting misinfo, persuasion & advertising Why would cognition include such a flaw? We argue it is a rational adaptation to high-quality info environments 🧵1/

Cameron Martel reposted

🚨New WP🚨 Remember Musk+Zuck+Trump+Jordan etc crying fact-checker bias b/c Reps were flagged more than Dems? We analyzed Community Notes on Musk's X and guess what: posts flagged as "misleading" are 67% more likely to be written by Reps! The issue is Reps, not fact-checkers... osf.io/preprints/ps...

Cameron Martel reposted

Three new articles discuss Meta’s decision to drop fact-checkers and shift to a "Community Notes" model, sparking concerns about misinformation. What's at stake? Here are three smart takes from experts. A short thread: 🧵

Cameron Martel reposted

🚨OpEd+data: Meta is out of step with public opinion🚨 Zuck cut moderation b/c he said people no longer want it. But he's wrong! We polled 1k Americans and most people, including majority of Reps: i) want content moderation ii) don't want Community Notes w/o fact-checkers thehill.com/opinion/tech...

Cameron Martel reposted

🚨New WP🚨 We examine news sharing on 7 platforms: 1)Right-leaning platforms=lower quality news 2)Echo-platforms: Right-leaning news gets more engagement on right-leaning platforms, vice-versa for left-leaning 3)But low-quality news gets more engagement EVERYWHERE, even BlueSky! osf.io/preprints/ps...

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Overall our results demonstrate the complex underpinnings of online partisan assortment Partisans pref connect w like-minded others not only bc of recommendation algos - but bc of distinct info & social prefs Party assortment is an enduring & important feature of social networks

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We found: -50% of ppl in co-partisan condition who followed-back account mentioned same partisanship as motivation -58% of ppl in counter-partisan condition who ignored account mentioned diff partisanship as motivation

We also found: -Information & making friends were most mentioned as follow-back reasons -Curiosity also oft mentioned, esp for counter-partisan follow-back -Not wanting info (esp from counter-partisans) & id’ing account as a stranger were most mentioned rzns for ignoring accounts

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These results suggest: -More issue extreme & out-party disliking ppl are *less likely* to follow-back anyone who isn’t a co-partisan -More in-party affinitive ppl are *more likely* to follow-back anyone who isn’t a counter-partisan

Ppl also wrote free-responses as to why they made their decision to follow-back or ignore accounts We conducted exploratory text analyses using GPT4 on these explanations, filtering for answers longer than a few words & considered overall ‘coherent’ (n=515)

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We recruited n=990 Twitter-using participants on Lucid & asked them to complete these 4 political variable measures We then randomly assigned participants to suppose they’d been followed on Twitter by either a Dem, Rep, or politically neutral human-looking account (similar to our field design)

Ppl w ⬆️ issue polarization (& to lesser extent ⬆️out-party dislike) were less likely to follow-back neutral & counter-partisans, relative to co-partisans In contrast, ppl w ⬆️ in-party affinity were more likely to follow-back neutral & co-partisans, relative to counter-partisans

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Altogether, our Twitter field exp shows: -Users pref co-partisan tie formation not solely to see congenial political content, but also bc of a social pref for connecting w co-partisan humans -This social pref is equal parts in-party pref & out-party dispref

What underlying social motives are associated with these (dis)preferences? In a follow-up survey exp, we examine how 4 political covariates - issue polarization, out-party disliking, in-party liking, & political knowledge - may be assoc w pref follow-back behavior

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We find: -Pref follow-back of co-partisan vs counter-partisan explicit bot accounts (ev of simple content prefs) -Even *greater* pref follow-back in human-looking vs bot accounts (ev of additional social motivation)

For our human-looking accounts, we also find preference for following-back co-partisans is similar in magnitude for dispreference for following-back counter-partisans, relative to our politically neutral condition

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We pretested our profiles to confirm that: -Human-looking profiles were perceived more human than bot profiles -Human & bot profiles perceived as similarly informative Thus can attribute diffs in follow-back rates btw bot & human accounts to social factors beyond informativeness

We ID’d a politically balanced set of Twitter users who RT’d recent posts from Fox News or MSNBC (N=3,013) & randomly assigned users to be followed by one of our accounts over a 14 day period We then assessed our key outcome: whether users followed-back our accounts

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There is also much work on affective partisan attitudes & polarization - ppl like & trust co-partisans > counter-partisans. Pref follow-back may therefore also be driven by affective social prefs for connecting with *actual* fellow partisans (beyond simple content prefs)

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But the mechanisms underlying this partisan pref in tie formation remain unclear Do ppl like seeing politically agreeable *content* or do they also prefer actually *socially* connecting w co-partisans? And if so, is this social pref more about in-party pref or out-party distaste?

Selective political content exposure is well documented - ppl prefer to see politically agreeable, & avoid politically disagreeable, info. This could be one reason why ppl preferentially follow-back co-partisans online - to help cultivate politically congenial news feeds

In prior work, we’ve found that shared partisanship causally affects Twitter followership. Users are ~3X more likely to follow-back co-partisans (vs counter-partisans) bsky.app/profile/came...

Cameron Martel@cameronmartel.bsky.social

Americans are more likely to be socially connected with co-partisans. Previous work finds that shared partisanship causally promotes social tie *formation* - users 3x more likely to follow-back co-partisan accounts www.pnas.org/doi/10.1073/...

We’ve also found in Twitter & survey exps that users are ~12X more likely to block counter-partisans than co-partisans (& that Dems block Reps more than vice-versa) bsky.app/profile/came...

Cameron Martel@cameronmartel.bsky.social

🚨New in PNAS Nexus🚨 Twitter field exps: -Users block counter-partisans 12x more than co-partisans -Dems block Reps more than vice-versa Survey exps: -Why block? To not see blocked user's posts -Dem/Rep diff prob bc Dems block misinfo/toxic sharers more  academic.oup.com/pnasnexus/ar...

Cameron Martel reposted

🚨Out in Nature!🚨 Many (eg Trump JimJordan Musk Vance) have accused social media of anti-conservative bias - but is this accurate? We test empirically, and it's more complicated than you might think: conservatives ARE suspended more, but also share more misinfo www.nature.com/articles/s41...

And here is a Research Briefing summary of our work in Nature Human Behaviour from myself & @dgrand.bsky.social : rdcu.be/dT3dq

rdcu.beOnline misinformation warning labels work despite distrust of fact-checkersNature Human Behaviour - Could online warning labels from fact-checkers be ineffective — or perhaps even backfire — for individuals who distrust fact-checkers? Across 21 experiments, we...
Cameron Martel@cameronmartel.bsky.social

🚨New in Nature Human Behaviour🚨 Will misinfo warning labels backfire for ppl who distrust fact-checkers? No! Labels reduce belief in & sharing of false news even for those highly distrusting of fact-checkers - warning labels are a key tool for platforms! rdcu.be/dSHtF