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.

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...

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...

📣 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...

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

🚨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/...

Huge thank you to collaborators @mmosleh.bsky.social @eckles.bsky.social @dgrand.bsky.social

Comments, feedback, & suggestions appreciated as always!

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...

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

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

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

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

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

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)

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

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

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

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

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

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)

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)

🚨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

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...

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...

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...

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/

🚨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...

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: 🧵

🚨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...

🚨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...

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...

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

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

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

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)