Put the very same people in a better room and they start trusting each other again.
This is the idea behind Povi. Six decisions took this from research insight to product, and we've weighed each carefully against the best studies on all sides.
i
The stage is built for disagreement.
A Povi stream is a live conversation in front of a crowd, and each creator's stream holds open seating for other sides.
Across 27 randomized studies, meeting the other group reduced prejudice, and least in the largest trials.
doi: 10.1017/bpp.2018.25Fifteen minutes of cross-party discussion warmed feelings by ten degrees and doubled trust; the strongest partisans moved least.
doi: 10.1017/9781009042192A moderated, briefed weekend of cross-party talk warmed each party toward the other by more than thirteen degrees, and the most polarized moved most.
doi: 10.1017/S0003055421000642Twelve-minute video calls between opposing partisans softened how they saw each other when the topic was their perfect day. When the topic was the parties, nothing moved.
doi: 10.1126/sciadv.abn5515Confidence: StrongA meta-analysis, two field experiments and a national deliberative poll point the same way: conversation across the divide softens it when the room has structure. The honest distance: in those studies, researchers paired the participants and everyone talked. On Povi, a creator decides who sits across from them, and most of the room is watching.
ii
You can see who you are talking to.
Every creator carries a viewpoint label and a bio. And every user profile shows where its owner stands. At one time, we considered hiding all of it. But the evidence says show it.
Cross-party chats with the partner’s party listed moved both opinions and feelings; chats with no label moved neither reliably.
doi: 10.1038/s41562-023-01655-0Ten unmoderated minutes with party and position visible the whole time built sympathy, though nobody’s opinion was measurably shifted.
doi: 10.1038/s44271-023-00051-8An opposing argument landed harder when the profile beside it shared something nonpolitical with the reader; feeling close to the author nearly doubled the effect.
doi: 10.1073/pnas.2112552118Confidence: GoodIn both direct tests the label stayed on screen the whole conversation and the people in it still depolarized. What failed elsewhere was making identity the topic. So we treat the affiliation label as context, not the subject.
iii
The rules are in the room.
The ground rules sit inside every engagement surface, where anyone about to speak can read them, not on a buried policy page. Each creator can also set their own rules for their community.
Pinning the rules to a discussion raised newcomer compliance by eight percentage points and brought seventy percent more newcomers in.
doi: 10.1073/pnas.1813486116Across twenty-one million chat messages, newcomers imitated whatever they saw first; decency and hostility both propagate.
doi: 10.1145/2998181.2998277Confidence: StrongOne preregistered field experiment in a community of thirteen million, and twenty-one million messages of observational support. The open question is whether posted rules actually change people or just change who shows up. Either is a positive effect, though.
iv
Our algorithm broadens your perspective.
Most feeds are built to predict what you will react to. Ours is built around topics and the creators you personally love watching most. When it recommends something, it hands you viewpoints a step away from what you are currently watching, not an extreme. Nothing is promoted for the fight it might start or the engagement it might bait.
Posts attacking the other party were shared about twice as often as ones supporting their own. That is what a feed will naturally promote when optimizing for engagement, so we do not.
doi: 10.1073/pnas.2024292118One week of reordered X feeds was enough: people shown less partisan hostility warmed toward the other side, and people shown more cooled by about as much.
doi: 10.1126/science.adu5584An opposing argument landed harder when the profile beside it shared something nonpolitical with the reader; feeling close to the author nearly doubled the effect.
doi: 10.1073/pnas.2112552118Three months of chronological or diversified feeds changed what people saw but nothing about how they felt about the other party.
doi: 10.1126/science.abp9364 doi: 10.1038/s41586-023-06297-wRepublicans who spent a month seeing the other side’s loudest voices in their feed ended more conservative, not less. Exposure without conversation hardened them.
doi: 10.1073/pnas.1804840115Confidence: MixedThe studies agree on the disease and split on the cure. Engagement ranking favors contempt; that much is solid. But reordering a feed did little on its own, and raw exposure to the other side made things worse. So we stopped expecting a feed to do the healing itself. Povi is built around topics and people, not feeds. And when we do recommend views unlike yours, we mind the messenger: people take an opposing argument better when it comes from someone they have something in common with.
v
Our rulings are public.
Whenever Povi moderates something, our ruling is published: anonymized, with the reason and the principle behind it. It is part of our commitment to transparency. Creators also have their own moderators in their communities.
Across thirty-two million Reddit posts, people whose removals came with a reason were less likely to be removed again. Removals without reasons taught nothing.
doi: 10.1145/3359252Timeouts and bans deterred the specific behavior they punished. They did not create good behavior on their own.
doi: 10.1145/2998181.2998277Confidence: ObservationalThirty-two million posts point the same way, but nobody has tested public transparency itself. We publish the record anyway, because a ruling nobody can read is a rule nobody can check.
vi
You are meant to come back.
Research keeps finding the same thing: one good conversation works, and then it wears off quickly. But that is unsurprising, no one expects one gym workout to last a year. Povi is built on the bet that what fades after one conversation holds after fifty.
Twelve-minute video calls between opposing partisans softened how they saw each other when the topic was their perfect day. When the topic was the parties, nothing moved.
doi: 10.1126/sciadv.abn5515AI-suggested rephrasings made tense gun-control conversations go better in the moment. Three months later there was no trace of them either.
doi: 10.1073/pnas.2311627120The one treatment that ran a week instead of a single sitting. Fourteen exchanges with the other side left both sides less polarized. But nobody has measured months or a year.
doi: 10.1038/s41562-023-01655-0The longest anyone has looked: after cross-party video calls about policy, hostility fell and was still measurably lower three months later.
CRC TRR 190 Discussion Paper 575Confidence: UntestedEverything above was measured after one conversation or one week. Whether repeatedly coming back makes it stick is the question no study has asked yet. The evidence leans our way, and we intend to study it.
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The studies.
Each one once, with the conditions its effect held under and what its authors disclosed.
The room
- Finding
- Allport proposed in 1954 that contact between groups reduces prejudice under the right conditions, and by 2019 hundreds of studies claimed to confirm it. Paluck, Green and Green kept only the ones that randomized contact and measured its effect at least a day later, which left 27. Across those, contact reduced prejudice by an average of 0.39 standard deviations, a medium effect and a real one. But the estimate has weak spots the field rarely mentions. Thirteen of the 27 studied college students. Only six studied adults over 25. Only eleven concerned racial or ethnic prejudice, and not one tested interracial contact among adults. The largest, most precise studies found the smallest effects, the pattern that usually means small studies overstate. Allport’s conditions themselves have never been tested as moderators. The honest summary: contact works, moderately, variably, and mostly on the young.
- Grade
- Settled
- Design
- Meta-analysis of 27 randomized experiments.
- Effect
- 0.39 standard deviations on average, varying widely by study.
- Held for
- At least one day, by inclusion rule; longer in some studies.
- Conditions
- Thirteen of 27 studies were college students; six studied adults over 25; none tested interracial contact among adults over 25.
- What we built from it
- The premise. Every other decision on this page is about how to structure contact so it earns that average.
- Disclosure
- No funding or conflict statement in the paper.
- Finding
- Between November 2018 and July 2019, 553 adults recruited through Facebook ads came to 28 sessions in public libraries and community centers around Philadelphia. Each was randomly assigned to one of three small groups: an apolitical control that read and discussed an article about beach towns, a same-party group that read about how polarized Americans are, or a mixed-party group that read about surprising common ground between the parties. Everyone said whether they agreed with their article, then talked for about fifteen minutes. In the mixed groups, the feeling thermometer for the other party rose from 13 to 23; the share who trust the other party to do right at least half the time doubled, from 18 to 37 percent; the share who would be not at all upset if a child married across party lines rose from 33 to 49 percent. A week later the effects were still there, among the 35 percent who answered. Strong partisans barely moved. The same-party groups did not change, and the authors note the treatment bundles the article with the discussion; an article-only test did much less.
- Grade
- Supports
- Design
- Randomized lab-in-the-field experiment, 553 adults across 28 sessions; 464 partisans in the main analysis.
- Effect
- Out-party thermometer from 13 to 23; trust that the other party does right at least half the time from 18 to 37 percent; index up 0.08 on a 0 to 1 scale, a 17 percent drop in animus.
- Held for
- One week, at the follow-up 35 percent completed.
- Conditions
- In person, groups of nonstudent adults, a shared article first, about fifteen minutes, one session. The authors note the article and the discussion were tested together; an article-only replication did much less.
- What we built from it
- The mixed room, with everyone able to speak. The shared reading before the talk is the one part we have not built.
- Disclosure
- Funded by the Annenberg Public Policy Center.
- Finding
- In September 2019, 523 registered voters drawn from NORC’s probability panel spent a weekend in Dallas. Before arriving they received a briefing book presenting evidence for and against 47 policy proposals, vetted by experts on both sides. Over the weekend they met in small groups of about thirteen, the same group throughout, with trained moderators who covered both sides and gave no hint of their own views; over 90 percent of participants strongly disagreed that their moderator had tried to influence them. Nothing was decided by show of hands; every judgment went into a confidential questionnaire. Democrats’ rating of Republicans rose from 19.5 to 32.6 degrees. Republicans’ rating of Democrats rose from 20.2 to 34.6. A control group of 844 from the same panel, who read nothing and discussed nothing, did not move. The most extreme participants warmed most; Republicans who held the maximum position on sixteen or more polarized questions warmed 24 degrees. On issues, the parties moved closer on 22 of the 26 most polarized proposals. A footnote reports some effects faded in later waves while differences in voting intention persisted more than a year.
- Grade
- Supports
- Design
- Deliberative Poll with pre-post control group; 523 participants, 844 controls, both from NORC’s probability panel.
- Effect
- Democrats’ rating of Republicans from 19.5 to 32.6; Republicans’ rating of Democrats from 20.2 to 34.6; parties converged on 22 of 26 polarized proposals.
- Held for
- Some effects faded in later waves; differences in voting intention versus control persisted more than a year.
- Conditions
- In person, one weekend, groups of about thirteen, trained moderators who gave no hint of their views, balanced briefing, expert plenaries, no show of hands.
- What we built from it
- Moderators in every stream, and a host who runs the room rather than the outcome. The briefing book is not built.
- Disclosure
- Helena Group Foundation. No competing interests declared.
- Finding
- In February 2020, Democrats and Republicans from a YouGov panel were invited, without knowing why, to test a new social media app. The app paired each with an opposing partisan under a gender-neutral pseudonym for a week of asynchronous text conversation about immigration or gun control, with fourteen replies required, a moderation team on staff, and block and report available. A placebo group wrote an essay from the same prompts. Compared with them, the chat group came out 0.22 standard deviations less polarized on a combined index. Split the index and the honesty problem appears: opinions moved significantly (0.19), feelings did not (0.14). The partner’s party label was randomized. Shown correctly, the effect was 0.25 and feelings cleared significance (0.21). Shown wrongly, 0.26. Not shown at all, 0.15 and not significant, with feelings near zero. Republicans moved 0.38, Democrats 0.09, a difference not itself significant. More civil partners produced more depolarization, but civility was not randomized. The authors caution that a group setting, where one side can gang up or collect likes, may not behave like a private pair.
- Grade
- Supports
- Design
- Randomized field experiment on a purpose-built app, 1,419 in the analysis; identity label randomized to correct, incorrect or absent.
- Effect
- 0.22 standard deviations on the combined index (issues 0.19, significant; feelings 0.14, not); with correct labels 0.25 (feelings 0.21, significant); with no label 0.15, not significant. Republicans moved 0.38, Democrats 0.09.
- Held for
- Measured at the end of the week; no later follow-up.
- Conditions
- Anonymous, text, asynchronous, one contested topic, fourteen replies required over a week, block and report available, moderators on staff. One-to-one, not a group.
- What we built from it
- The viewpoint label that stays on screen, and the bet that a week of contact is worth more than a session.
- Disclosure
- Funded in part by a Facebook Foundational Research Award; funders had no role in design or publication. One author consulted for Twitter in 2022.
- Finding
- Labour and Conservative voters recruited on Prolific were screened for the issues that divided them most, immigration and redistribution, then matched by algorithm to an opposing voter for a ten-minute text chat about one of them. The partner’s party and issue position stayed on screen throughout. No moderator, no tips. A wait-list control group talked later, so mechanisms could be tested without contamination. Of 777 who showed up, 582 could be matched in time and were analyzed. Sympathy for opposing voters rose 1.19 points on an eleven-point scale, d = 0.43, and willingness to befriend and to talk with them rose too. Opinions on the issue did not change. The effect shrank as the pair’s positions grew further apart. It tracked with feeling understood, feeling included, perceiving common ground, and correcting misperceptions of the other side. The author cites a Dutch version of the platform whose link was clicked more than 300,000 times as evidence the format scales.
- Grade
- Supports
- Design
- Randomized wait-list experiment, 582 UK voters recruited on Prolific.
- Effect
- Sympathy up 1.19 points on an 11-point scale, d = 0.43.
- Held for
- Measured immediately after; no long follow-up in the paper.
- Conditions
- Text, ten minutes, no moderator, no tips, party and issue position visible, disagreement varied by matching.
- What we built from it
- Viewpoint shown throughout, never hidden mid-conversation.
- Disclosure
- No competing interests declared.
Braghieri, Schwardmann and Tripodi, 2026Working paper. Not peer reviewedCRC TRR 190 Discussion Paper 575
- Finding
- US Democrats and Republicans recruited online took a quiz on policy-relevant facts, then had a video conversation with either a co-partisan or a counter-partisan, then could revise their answers. Before pairing, they were asked how much they would pay to choose their partner: they preferred their own side, and expected cross-party calls to be less informative and less pleasant. They were wrong about the pleasantness: afterward they rated both kinds of conversation equally enjoyable. Cross-party conversations were similar in content and tone to same-party ones. They were partly right about the information: people learned less from counter-partisans, because they did not trust what the other side knew. Across 499 conversations, 243 of them cross-party, affective polarization fell by 0.14 to 0.33 standard deviations across five measures and the thermometer gap narrowed about 20 percent. More than three months later, in a follow-up disguised as an unrelated survey and completed by 682, the reduction was still there. More emotionally engaged conversations produced larger reductions. The paper is dated June 2026 and has not been peer reviewed.
- Grade
- Supports
- Design
- Preregistered experiment, 499 video conversations (243 cross-party), participants recruited online; obfuscated follow-up of 682.
- Effect
- Affective polarization down 0.14 to 0.33 standard deviations across five measures; thermometer gap narrowed about 20 percent.
- Held for
- Three months.
- Conditions
- Video, policy-fact topic, one conversation, partner assigned.
- What we built from it
- The premise of plate vi. This is the study that says the warming can outlast the call.
- Disclosure
- Funded by CIVICA and the Deutsche Forschungsgemeinschaft (CRC TRR 190).
- Finding
- Strangers from opposite parties were paired for video calls on a platform built for cross-party conversation, with no moderator, no briefing, no instructions and a randomly assigned topic. In the first study, 478 people talked for a median of twelve minutes about their perfect day. Warmth toward the other party rose by 0.34 standard deviations, about nine degrees, which the authors describe as reversing roughly two decades of increase. Three months later it was gone: d = -0.02. The second study, 338 people recruited through Facebook ads, added two political topics: what you like about your own party, and what you dislike about the other. Neither warmed anyone. The perfect-day calls did (0.41). The dislike conversations ran longest and raised the perceived importance of cross-party dialogue, but produced no significant warmth. The authors note their political prompts were about the parties, not about any issue, that they never tested issue conversations, and that no conversation turned hostile. The finding is no gain, not harm.
- Grade
- No effect
- Design
- Two randomized experiments on a video platform, 478 and 338 participants.
- Effect
- Perfect-day conversations d = 0.34 and 0.41; party-topic conversations not significant; at three months d = -0.02.
- Held for
- Zero at three months.
- Conditions
- Video, strangers, no moderator, no structure, one call; the disagreement prompts were about the parties, not an issue.
- What we built from it
- The reason a stream is about a topic, never about the parties.
- Disclosure
- Civic Health Project. Platform hosted by AllSides. No competing interests declared.
Who is speaking
- Finding
- In a large preregistered experiment on Amazon Mechanical Turk, each participant was matched to a peer, with the match varied along two lines: how similar they were on nonpolitical traits, and whether they agreed about redistributing wealth. Participants saw the peer’s profile, read a short persuasive essay by the peer on reducing inequality through redistribution, then gave their own view. Reading a stranger’s argument for redistribution raised support for it and reduced polarization, for people with mild views and with strong ones, on both sides. The mechanism was the profile. When the author shared nonpolitical traits with the reader, the reader was more receptive to an argument they disagreed with; feeling close to the author was associated with an 86 percent larger effect. One essay, one reading, no reply, no follow-up. The result is about exposure to a person, not conversation with one.
- Grade
- Supports
- Design
- Large preregistered online experiment on Amazon Mechanical Turk.
- Effect
- Feeling close to the author associated with an 86 percent larger effect.
- Held for
- One session; no follow-up.
- Conditions
- One-way reading, no conversation, one topic, profile shown before the essay.
- What we built from it
- Creator bios and profiles that show more than a party.
- Disclosure
- The authors declare no competing interest.
- Finding
- The only live-chat study in the set. The authors captured about 21 million messages from about 600 English-language Twitch channels over nine days in March 2016 and coded three behaviors: spam, questions and smiles. Whatever appeared in chat, more of it followed. After one spam message, spam in the next ten messages rose 44 percent; after one question, questions rose 55 percent; after one smile, smiles rose 220 percent. Who set the example mattered. Moderators were imitated most: spam after a moderator’s spam rose 68 percent, smiles after a moderator’s smile rose 333 percent. Subscribers were imitated more than regular users. Twitch Turbo users, who carry site-wide status but no stake in any channel, were imitated less than regular users. Bans worked as deterrents: spam after a banned spam message rose 13 percent, against 47 percent after an unpunished one. Chat modes cut spam by 14 to 23 percent. No moderation tool increased good behavior. Only a good example did. Imitation faded over roughly fifty messages, and the study could not see the video the chat was reacting to.
- Grade
- Supports
- Design
- Observational interrupted time series, about 21 million messages over nine days.
- Effect
- After one example, smiles up 220 percent, spam up 44 percent in the next ten messages; after a moderator’s example, larger still. Spam after a banned message up 13 percent versus 47 percent after an unpunished one.
- Held for
- Imitation faded over roughly fifty messages.
- Conditions
- Live chat, English-language channels, video not captured.
- What we built from it
- Moderators in every stream, and rules at the top of the room, because the first messages set the tone.
- Disclosure
- NSF funded.
Rules in the open
- Finding
- On r/science, a Reddit community of thirteen million where newcomers wrote a third of the comments and more than half of the ones moderators removed, moderators and a researcher tested a sticky comment. For thirty days in August and September 2016, software randomly assigned each new discussion of a scientific article either a pinned message stating the rules, the consequences, the fact that most people agree with them and the number of moderators enforcing them, or nothing. More than a thousand moderators, blinded to the assignment, kept removing rule-breaking comments as usual. Across 2,190 discussions and 18,264 newcomer comments, the pinned rules raised a newcomer’s chance of complying from 52.5 percent by 8.4 percentage points. They also raised the number of first-time commenters in a discussion by 70 percent. Because the software could not see who read the rules and chose not to post, the author cannot separate whether the rules changed people’s behavior or changed who decided to join. Both moved. Removals rose too, since more newcomers came, though that estimate did not survive every test.
- Grade
- Supports
- Design
- Preregistered field experiment, 2,190 discussions over thirty days, run with the community’s moderators.
- Effect
- Newcomer rule compliance up 8.4 percentage points from a 52.5 percent base; newcomer participation up 70 percent.
- Held for
- The thirty days of the experiment, per discussion.
- Conditions
- One large forum, rules posted inside the thread, over a thousand moderators blinded to condition.
- What we built from it
- Rules posted inside every room, and creators’ own rules alongside them.
- Disclosure
- No conflict declared. Data and code public.
- Finding
- The authors analyzed 32 million Reddit posts to ask whether telling people why their post was removed changes what they do next. Removal explanations, when they exist, mostly teach community norms. Controlling for how active a user was and how often they had been removed before, a one standard deviation increase in the share of a user’s removals that came with an explanation lowered the odds of their future posts being removed by about 6.5 percent, and by 30 to 35 percent in some large communities. Explanations from human moderators did no better than explanations from bots, and AutoModerator wrote about 95 percent of the automated ones. Explanations did not change whether people kept posting at all. Removals with no explanation taught nothing. The study is observational, on one platform, and the explanations it measures were attached to the removed post itself.
- Grade
- Supports
- Design
- Observational, 32 million Reddit posts.
- Effect
- One standard deviation more explanation lowered the odds of future removal by about 6.5 percent; 30 to 35 percent in some large subreddits.
- Held for
- Users’ subsequent posts across the dataset.
- Conditions
- Reddit, reasons attached to the removed post itself, no randomization.
- What we built from it
- The transparency report: Povi’s rulings published with the reason and the principle. His reasons were inline; ours are after the fact.
- Disclosure
- NSF grant IIS-1553376.
- Finding
- In October 2022, 1,574 people who disagreed about gun regulation were paired for text chats. In treated chats, one partner’s messages went through a language model that offered rewrites, restating, validating or softening, which the writer could accept, edit or ignore. Of 2,742 suggestions, two thirds were accepted. The topic of messages did not change; the tone did. Partners of treated participants rated the conversation 4 percentage points higher in quality and were 2.5 points more willing to grant the other side legitimacy, rising to about 6 points at full dose. The effect was largest where disagreement started highest. Nobody’s policy position moved. Only 44 percent of chats ran the intended length, and at a three-month follow-up there was no evidence anything had persisted.
- Grade
- Supports
- Design
- Preregistered field experiment, 1,574 participants.
- Effect
- Conversation quality up 4 percentage points, democratic reciprocity up 2.5, about 6 at full dose. Two thirds of suggestions accepted.
- Held for
- No evidence of persistence at three months.
- Conditions
- Text, one topic, one session, suggestions optional; only 44 percent of chats ran full length.
- What we built from it
- Nothing yet.
- Disclosure
- NSF, Duke, BYU. No competing interests declared.
Feeds
- Finding
- The authors collected 2,730,215 posts from news outlets and members of Congress on Facebook and Twitter and coded their language for references to the political in-group and out-group, positive and negative emotion, and moral-emotional words. Posts about the out-group were shared or retweeted about twice as often as posts about the in-group. Each additional word about the out-group raised the odds of a share by about 67 percent. Out-group language predicted engagement better than negative emotion or moral-emotional language, the two things prior work had blamed, and it was the strongest predictor of the “angry” reaction, the most common reaction on the posts studied. The study describes what spreads; it does not test what a feed does to a person.
- Grade
- Supports
- Design
- Observational, 2,730,215 Facebook and Twitter posts.
- Effect
- Each additional out-group word raised the odds of a share by about 67 percent.
- Held for
- Not applicable; a description of the feeds, not an intervention.
- Conditions
- US media and congressional accounts, dictionary-coded language.
- What we built from it
- No engagement ranking. Discovery by topic.
- Disclosure
- Gates Cambridge, John Templeton Foundation, UK Economic and Social Research Council, UK Cabinet Office. No competing interest declared.
- Finding
- In July and August 2024, 1,256 X users whose feeds were at least 5 percent political installed a browser extension that intercepted the For You feed and reordered it in real time. A classifier flagged political posts; a language model scored each for eight markers of antidemocratic attitudes and partisan animosity. For one group, flagged posts were pushed down, about 85 a day. For another, flagged posts drawn from other participants’ feeds were pushed up, about 11 a day. After one week, people who saw less animosity rated the other party 2.1 degrees warmer, and people who saw more rated it 2.5 degrees cooler; in-feed measures moved more, 3.2 and 2.6. The authors size that as about three years of the national trend, in one week. Anger and sadness moved during the week and did not persist. Time on platform, reposts and favorites did not change. Political posts drew more engagement than nonpolitical ones, but animosity posts drew no more than other political posts. Three quarters of participants noticed no change. Effects were measured the day after treatment ended; two thirds of the sample were Democrats; the platform was not involved.
- Grade
- Supports
- Design
- Two parallel preregistered randomized experiments, platform-independent.
- Effect
- Warmth up 2.1 points when animosity was reduced, down 2.5 when increased; the authors size it as about three years of the national trend.
- Held for
- Measured the day after a one-week treatment; in-feed emotion effects did not persist.
- Conditions
- X, desktop only, users with at least 5 percent political content, two thirds Democrats.
- What we built from it
- Recommendations that never promote a post for the fight it will start.
- Disclosure
- Run without the platform’s involvement.
- Finding
- In October 2017, Republicans and Democrats who visited Twitter at least three times a week were offered payment to follow a bot for a month. For Republicans the bot retweeted 24 messages a day from liberal elected officials, opinion leaders, media and nonprofits; for Democrats, the conservative equivalents. Weekly quizzes checked whether they were reading. The outcome was a scale of issue attitudes from liberal to conservative. Republicans came out more conservative: 0.12 points on a seven-point scale across everyone assigned, rising to 0.60 among the 53 who fully complied, an increase of 0.11 to 0.59 standard deviations. Democrats moved slightly liberal, not significantly. The study measured opinions, not feelings about the other party; the largest estimate rests on 53 people; and the exposure was to the other side’s most prominent voices, with no conversation attached.
- Grade
- Cuts against
- Design
- Preregistered field experiment; 1,652 pretreatment responses.
- Effect
- Republicans up 0.12 on a seven-point conservatism scale overall, 0.60 among the 53 who fully complied.
- Held for
- Measured at the end of the month.
- Conditions
- Twitter, 2017, 24 retweets a day, exposure with no conversation.
- What we built from it
- The step-not-a-leap recommendation, and conversation instead of exposure.
- Disclosure
- Carnegie Corporation, Russell Sage Foundation, National Science Foundation. No conflict declared.
Guess et al. and Nyhan et al., 2023Science, and Naturedoi: 10.1126/science.abp9364 doi: 10.1038/s41586-023-06297-w
- Finding
- Two experiments run inside Facebook and Instagram from late September to late December 2020, with consenting adult users. Guess switched 23,391 Facebook and 21,373 Instagram users to a reverse-chronological feed. Time on platform fell sharply; on Facebook, political content rose 15 percent, content from untrustworthy sources rose 69 percent, and uncivil content fell 43 percent. Nyhan downranked content from like-minded friends, pages and groups for 23,377 Facebook users, cutting like-minded sources from 54 to 36 percent of the feed and uncivil content from 3.15 to 2.81 percent; cross-cutting exposure rose only from 21 to 28 percent, with neutral sources filling most of the gap. Neither experiment moved affective polarization, issue polarization, or any of the other attitudes preregistered. Guess was powered to detect 0.032 standard deviations; Nyhan 0.019, with seven of eight estimates within 0.03 of zero and anything beyond 0.12 ruled out. Nyhan also reports that the median user got half their feed from like-minded sources, while political and news content were about 7 percent of what anyone saw. The authors note three months during an election may be too short for downstream effects.
- Grade
- No effect
- Design
- Two preregistered randomized experiments; 44,764 users in Guess, 23,377 in Nyhan.
- Effect
- Zero on affective polarization; Nyhan could detect 0.019 standard deviations and rule out anything beyond 0.12.
- Held for
- Three months of treatment, measured in post-election waves.
- Conditions
- Inside the platforms, during a polarized election, consenting users.
- What we built from it
- Nothing directly. It is the reason this page never claims the feed does the work.
- Disclosure
- Run in collaboration with Meta, which paid research costs and provided the data; academic authors were not paid by Meta and held final control of the analysis and text; Meta could not block publication. Several academic authors disclose prior Meta relationships; some authors are or were Meta employees. Two errata to Guess, neither affecting the estimates.
- Finding
- In the four weeks before the November 2018 midterms, 1,661 Facebook users, younger and more Democratic than average and heavier users, were randomly offered payment to deactivate their accounts; 580 did, with compliance verified on 90 percent of checks. A political polarization index fell 0.16 standard deviations, driven by two components, exposure to congenial news and polarization on issues, both significant. Feelings toward the other party fell 0.06 standard deviations, not significant. News knowledge fell 0.19. Subjective well-being rose 0.09. After the experiment, participants used Facebook less. The authors benchmark the 0.16 against a 0.38 rise in polarization from 1996 to 2016; leaving the platform for a month undid about 42 percent of it on issues, and could not be shown to have touched feelings.
- Grade
- No effect
- Design
- Preregistered randomized experiment, 1,661 in the impact sample.
- Effect
- Polarization index down 0.16 standard deviations, driven by issues and news exposure; affective polarization -0.06, not significant; news knowledge -0.19; well-being +0.09.
- Held for
- Measured at the end of the four weeks.
- Conditions
- One platform, heavy users, younger and more Democratic than average, paid to leave.
- What we built from it
- Nothing directly. It keeps us from claiming the platform is the disease.
- Disclosure
- Sloan and Knight Foundations. One author a paid employee of Microsoft Research; one paid consulting for Amazon and part of a Microsoft-funded network.
Compiled by Povi.
Last updated September 2026.