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Cameron Martel

@cameronmartel.bsky.social
441 followers 278 following 104 posts

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

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Reposted by Cameron Martel
Kate Klonick @klonick.bsky.social · 04/12/2025
Holy shit. Reuters reporting that new admin instructions on visas are if you worked at a platform in trust & safety or content moderation or on fact checking or online safety at an platform you *and your loved ones* are ineligible for H-1B visa. www.reuters.com/world/us/tru...
The cable, sent to all U.S. missions on December 2, orders U.S. consular officers to review resumes or LinkedIn profiles of H-1B applicants - and family members who would be traveling with them - to see if they have worked in areas that include activities such as misinformation, disinformation, content moderation, fact-checking, compliance and online safety, among others.
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Johan Ugander @jugander.bsky.social · 12/09/2025
📣 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.com
New Directions in Social Algorithms Research on October 16-17, 2025 at Yale University
As 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...
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Jenny Allen @jennyallen.bsky.social · 06/08/2025
New in TiCS w @dgrand.bsky.social @gordpennycook.bsky.social 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
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David Rand @dgrand.bsky.social · 17/06/2025
🚨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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
Huge thank you to collaborators @mmosleh.bsky.social @eckles.bsky.social @dgrand.bsky.social Comments, feedback, & suggestions appreciated as always!
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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.org
Perverse 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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
Our results demonstrate social media’s ability to foster engagement w corrections via minimal social relationships Ppl are more likely to engage w those who have followed & engaged w them first
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
We next conducted a follow-up survey on MTurk to replicate effects in a more controlled setting (eg eliminate blocking of counter-partisan bots) & obtained similar results
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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.com
Blocking of counter-partisan accounts drives political assortment on Twitter
Abstract. There is strong political assortment of Americans on social media networks. This is typically attributed to preferential tie formation (i.e. homo
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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.org
PNAS
Proceedings 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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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
Each user was then socially corrected by their randomly assigned bot. Social corrections were done via public reply to the tweet containing the debunked URL and included a link to the fact-check on @snopes.com
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
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)
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
Social corrections, where users correct one another on social media, have been found to be effective in survey settings shorturl.at/SYomc But in the field, social corrections are often ignored shorturl.at/jcxPd We ask what *causes* greater engagement on Twitter (X)
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Cameron Martel @cameronmartel.bsky.social · 14/04/2025
🚨New in @plosone.org🚨 Corrections of misinfo are often ignored. What can drive engagement? Twitter field exp & survey followups find -Social ties matter: users more likely to engage w corrections from accounts who followed user -Shared partisanship had smaller effects on engagement shorturl.at/0Ycdp
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Reposted by Cameron Martel
Oxford Internet Institute @oii.ox.ac.uk · 13/03/2025
In the latest episode of the OII podcast, we're tackling misinformation and polarization on social media. What's the real-world impact? How are governments responding? And what about AI and deepfakes? Listen here: podcasts.ox.ac.uk/why-social-m...
podcasts.ox.ac.uk
Why social media is the new frontier for misinformation, and what we can do about it: Professor Mohsen Mosleh and Cameron Martel
In the sixth episode of the OII Podcast, our experts discuss topics such as: * The real world impacts that arise when people increasingly identify with their political tribes online * What role govern...
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David Rand @dgrand.bsky.social · 06/03/2025
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.org
Fact-checker Warnings Are Surprisingly Effective Even For Skeptics | SPSP
Even when people distrust fact-checkers, they’re still influenced by warning labels on false news.
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Reposted by Cameron Martel
Reed Orchinik @rorchinik.bsky.social · 01/02/2025
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/
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David Rand @dgrand.bsky.social · 31/01/2025
🚨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...
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Reposted by Cameron Martel
Matt DeVerna @matthewdeverna.com · 29/01/2025
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: 🧵
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David Rand @dgrand.bsky.social · 28/01/2025
🚨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...
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David Rand @dgrand.bsky.social · 09/12/2024
🚨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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
HUGE thank yous to project co-lead @mmosleh.bsky.social (at @oiioxford.bsky.social) & @dgrand.bsky.social Thoughts, comments, & feedback welcome and appreciated as always!! Paper here: dx.doi.org/10.1037/xge0... Preprint here: osf.io/preprints/ps...
dx.doi.org
APA PsycNet
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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)
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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)
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
We examine this in a Twitter field exp. We created 3 *explicit bot* accounts and 3 *human-looking* accounts, varying only in their expressed party ID (⅓ Dem, Rep, Politically Neutral)
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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?
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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...
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
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...
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Cameron Martel @cameronmartel.bsky.social · 16/10/2024
🚨New in JEP:G🚨 Why do ppl preferentially reciprocate follows by co-partisans online? In a Twitter field exp & online survey exp we find: -Both content *and* social prefs drive co-party tie-making -Distinct roles for in-party pref & out-party dispref dx.doi.org/10.1037/xge0...
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David Rand @dgrand.bsky.social · 02/10/2024
🚨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...
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Ethan Porter @ethanvporter.bsky.social · 19/09/2024
In a new article in Political Communication, @mattgraham.bsky.social and I study how to increase readership of fact-checks. It's hard! Social pressure, civic duty and small payments help; leveraging Party ID doesn't. www.tandfonline.com/doi/full/10.... Ungated: osf.io/preprints/os...
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Cameron Martel @cameronmartel.bsky.social · 18/09/2024
And here is a Research Briefing summary of our work in Nature Human Behaviour from myself & @dgrand.bsky.social : rdcu.be/dT3dq
rdcu.be
Online misinformation warning labels work despite distrust of fact-checkers
Nature 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...
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David Rand @dgrand.bsky.social · 12/09/2024
🚨Out in Science!🚨 Conspiracy beliefs famously resist correction, ya? WRONG: We show brief convos w GPT4 reduce conspiracy beliefs by ~20%! -Lasts over 2mo -Works on entrenched beliefs -Tailored AI response rebuts specific evidence offered by believers www.science.org/doi/10.1126/...
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