The Outrage Dividend — What Social Media Did to Trust

ENSI Research · Built on a library of 46 primary documents

The argument, before the list

The public conversation about social media and trust has swung between two lazy positions. The first says the platforms broke democracy: algorithms radicalise, lies outrun the truth and trust collapses as a result. The second, which has gained ground since the large Meta election experiments of 2020, says the panic was overdone: take away the algorithm and nothing much changes, so the problem must lie somewhere else. Both positions treat social media as a cause that either is or is not doing the damage. The evidence in this library supports a third reading, and it is the spine of this report. Social media is an amplifier and a reward system laid over an older dynamic of grievance and blame. It did not create the dynamic. It changed the terms of trade.

Consider what the strongest studies actually measure. When a post from a US news outlet or a member of Congress names the political out-group, each such term raises the odds that the post is shared by 67% (Rathje et al. 2021). Out-group language beats negative emotion by a factor of about 4.8 and moral-emotional language by about 6.7. The single most rewarded thing a political actor can do online is not to be angry in general. It is to point at them. That is blame, in the precise sense that Malle and colleagues give it: a judgement about an agent, aimed at a target and seeking an audience (Malle et al. 2014). The engagement economy does not reward emotion as such. It rewards the attribution of fault to an out-group.

The same reward then trains the people who post: more likes for outrage today means more outrage tomorrow (Brady et al. 2021). And engagement-ranked feeds amplify out-group animosity beyond what users chose to follow, while users rate the political posts so selected as worse than their chronological feed (Milli et al. 2025). The platform selects for content its own users, asked reflectively, say they do not want.

So why do the large field experiments find so little? A three-month switch to chronological feeds for some 44,000 Facebook and Instagram users in 2020 did not measurably change polarisation, knowledge or trust (Guess et al. 2023). Four weeks off Facebook did not significantly reduce affective polarisation (Allcott et al. 2020). Across twelve OECD countries, online news use is unrelated to the trend in polarisation, and US polarisation began rising decades before Facebook (Boxell et al. 2020). These results are real. But they test a narrow question — what happens to one individual for a few weeks when their feed changes while everyone else’s stays the same. They cannot see what the incentive structure does to what politicians, outlets and creators produce for everybody. Guess and colleagues say so themselves.

The reconciliation is therefore not “it is all the algorithm” or “it is nothing”. The grievance, the identity sorting, the elite polarisation and the distrust were already there. What social media did was change the economics of acting on them. It lowered the cost of blaming (no face, no retaliation, a crowd to hide in), enlarged the audience for the blamer (networks of hundreds instead of a dinner table) and made the reward visible and immediate (a counter that goes up). Crockett put it in one line: digital media inflate the triggering stimuli of outrage, reduce some of its costs and amplify many of its personal benefits (Crockett 2017). The consequence for trust follows the in-group and out-group split. Outrage raises the expresser’s standing and perceived trustworthiness inside the group while eroding trust across groups — through dehumanisation, false polarisation and the misperception of how hostile the other side really is (Brady, Crockett & Van Bavel 2020; Finkel et al. 2020).

This has a direct implication for what to fix. If social media were the root cause, the remedy would be to remove it or clean up the content. If it is an amplifier and a reward system, the remedy is to change what is rewarded and how content is ranked — and to address the grievances it amplifies at their source. Content moderation chases the output; incentive and ranking design changes the production function. Field reranking experiments support this (Piccardi et al. 2024).

The stakes are not only American. Social and video networks have overtaken news sites as the main route to online news across 48 markets, and trust in news met there sits well below trust in news overall (Reuters Institute 2026); 70% of people across 28 countries are hesitant or unwilling to trust someone different from them (Edelman 2026). For a mid-sized EU state with a fragmented party system and a news market already migrating to platforms, the question is not whether social media “causes” distrust. It is whether the national information environment pays a dividend to people who blame, and what a state can do about the price.

Summary of the main points

• Out-group blame is the engagement currency: each out-group term raises sharing odds by 67%, far more than emotional language (Rathje et al. 2021).

• Engagement ranking over-supplies division: out-group animosity +0.24 SD, political anger +0.75 SD, yet lower user value for political picks (−0.18 SD) (Milli et al. 2025); reranking moves out-party warmth by ±2–2.5° in a week (Piccardi et al. 2024).

• Outrage is learned through feedback and network norms (Brady et al. 2021).

• Platforms lower the cost and raise the reward of outrage (Crockett 2017; Brady, Crockett & Van Bavel 2020).

• The blame machine changes who can blame whom — stranger, audience-directed blame without warrant checks (Malle et al. 2014).

• Moral contagion is real but modest: +13% shares per moral-emotional word, not +20% (Brady et al. 2025).

• Human attention amplifies too: falsehood is 70% more likely to be retweeted (Vosoughi et al. 2018); each negative headline word lifts clicks 2.3% (Robertson et al. 2023).

• Ranking amplifies tone, not extremes, and favours the mainstream right in 6 of 7 countries (Huszár et al. 2022).

• The end state is sectarianism: US out-party hate now exceeds in-party love (Finkel et al. 2020).

• Trust is going insular and platform-mediated: news trust 37%, on social media 22%, in AI answers 20% (Reuters Institute 2026).

• What the evidence does not show: that platforms caused the rise in polarisation, that short feed swaps change attitudes, that cross-cutting exposure heals, or that misinformation is the core problem.

• Verdict: an amplifier and reward system for an existing grievance-and-blame dynamic. Fix incentives and ranking, not only content.

How this is organised

The ten mechanisms below are ranked by three criteria taken together. The first is the strength of the evidence — pre-registered, large-sample and causal designs rank above correlational ones and above frameworks. The second is proximity to trust and blame — how directly the mechanism bears on whom people blame and whom they trust. The third is leverage — whether the mechanism sits in something that can be designed or regulated, such as a ranking objective or a reward signal, rather than in human nature. Each mechanism is set out in three parts: what the evidence shows (with exact figures and samples), stakes for trust, and what to watch. A full section then sets out what the evidence does not show, before the verdict.

1. Out-group animosity is the strongest engagement predictor

What the evidence shows. Rathje, Van Bavel and van der Linden analysed 2,730,215 posts from US news media and members of Congress on Facebook and Twitter (Rathje et al. 2021). The media datasets held 599,999 Facebook posts and 227,229 tweets; the Congress datasets 1,078,562 tweets and 825,424 Facebook posts. The headline is stark: “Each individual term referring to the political out-group increased the odds of a social media post being shared by 67%.” Posts about the out-group were shared about twice as often as posts about the in-group.

An internal meta-analysis across eight datasets puts the comparison in one line · out-group words: exp(b) = 1.67 · in-group words: 1.05, not significant · negative affect: 1.14 · moral-emotional words: 1.10 · positive affect: 0.95. The out-group effect is about 4.8 times the size of negative affect and 6.7 times that of moral-emotional language. Posts containing only out-group terms, compared with posts containing only in-group terms, were associated with a 132% increase in sharing.

Three further findings sharpen the point. First, elites amplify more: the out-group effect was exp(b) = 1.99 in the Congress datasets against 1.41 for media, and per-word effects across Congress datasets ranged from 65% to 180%. Second, the effect was not moderated by political orientation or platform. Third, out-group language predicted “angry” reactions (meta exp(b) = 2.19) and “haha” reactions — mockery — while in-group language predicted “love” (1.57). Anger was the most popular Facebook reaction in seven of the eight datasets. The authors draw the uncomfortable conclusion that people say they do not want leaders to express partisan animus, yet this is the content that receives the most engagement. They also note Facebook’s internal finding, reported in the press, that posts rated “bad for the world” drew more engagement and that down-ranking them cut engagement — and that the company “ultimately chose not to approve the feature”.

Stakes for trust. This is the single most important finding in the library for the question of blame. The strongest engagement signal is not how emotional a post is but who it blames. In-group warmth earns little; out-group derogation earns a great deal. A political actor who wants reach is paid to name an enemy. Since political elites capture the largest premium, the incentive acts on exactly the people whose rhetoric shapes followers’ norms. Rathje and colleagues add a point that runs against much of the policy debate: “the severity of online echo chambers appears to be a less important issue than the kind of content that tends to surface at the top of one’s feed”. The problem is less who people are connected to than what wins the competition for their attention.

What to watch. The share of political and party communication that names an out-group, and its engagement premium over in-group or policy content · whether elite accounts’ out-group premium widens in election periods · whether platforms publish the engagement performance of content their own raters judge harmful, as Facebook’s internal research did · non-US replications, since all of this evidence is American and from before 2020.

2. Engagement ranking amplifies divisive content users say they do not want

What the evidence shows. Milli and colleagues ran a pre-registered audit of 806 US Twitter users in February 2023 (Milli et al. 2025). They compared the first ten tweets in each user’s engagement-ranked timeline with the ten most recent tweets from accounts the user followed — holding constant who the user had chosen to follow — and surveyed users about each tweet. Across 26 outcomes, every result significant at p < 0.05 survived a false discovery rate of 0.01.

Relative to the chronological baseline, the algorithm amplified partisanship (+0.24 SD) and out-group animosity (+0.24 SD). Readers felt worse about their out-group (−0.17 SD) and better about their in-group (+0.08 SD). Author anger rose by 0.47 SD overall; in political tweets “anger was by far the predominant emotion amplified”, with author anger up 0.75 SD and reader anger up 0.37 SD. The decisive result concerns satisfaction. Overall, users rated the engagement-ranked tweets very slightly higher on wanting to see them (+0.06 SD). But for political content, the algorithm’s selections delivered significantly lower user value than the chronological feed (−0.18 SD, p = 0.005). The authors’ reading is that engagement ranking serves users’ revealed preferences — what they linger on and react to — but not their stated preferences.

Two field and lab experiments move this from audit to cause. In a pre-registered ten-day experiment on X during the 2024 US campaign, Piccardi and colleagues used a browser extension to rerank the real feeds of 1,256 participants (Piccardi et al. 2024). Cutting posts that expressed anti-democratic attitudes and partisan animosity from about 10% of the feed to about 1% warmed feelings toward the out-party by +2.11° on a 100-point thermometer. Raising their share from about 10.6% to 13.4% cooled them by −2.48°. The effects did not differ significantly by party, and the authors equate them to roughly three and 3.6 years of the US polarisation trend. In-feed, reduced exposure lowered anger by about 5 points and increased exposure raised it by a similar amount. In a pre-registered lab study of 1,380 US partisans, Jia and colleagues found that both downranking and a plain chronological feed reduced partisan animosity relative to an engagement-ranked feed (d ≈ −0.20 to −0.25), and concluded that “status quo engagement-based ranking may pose the greatest risks to democratic attitudes” (Jia et al. 2023).

Stakes for trust. This mechanism converts the abstract complaint about “the algorithm” into a specific, testable charge: the engagement objective systematically over-supplies hostile content relative to what users endorse on reflection. That is a trust problem twice over. It cools feelings toward the other side, and it erodes trust in the platform itself as a faithful agent of the user. A system that people experience as feeding them what they would rather not see invites the cynical “they are manipulating us” reading that spreads to institutions generally. The Piccardi result also shows that reranking does not require removing speech. Hostile posts were down-weighted, not deleted, and 74.2% of participants noticed no impact on their experience.

There is a business tension: reduced exposure cut time on X by 4.71 minutes a day (Piccardi et al. 2024), and bridging-oriented ranking is hardest to sustain under competition for attention (Ovadya & Thorburn 2023). And ranking by stated preference alone reduced hostility mainly by showing fewer out-group tweets — trading outrage for an echo chamber — whereas a tie-breaker that down-weights out-group animosity avoided that cost (Milli et al. 2025).

What to watch. The gap between revealed-preference (engagement) and stated-preference (survey) metrics in platform reporting · whether ranking objectives include an explicit down-weight on out-group animosity · independent, platform-free reranking experiments of the Piccardi type outside the US · the engagement cost platforms report when testing such changes, since that number decides whether they survive without regulation.

3. Outrage is rewarded — and learned

What the evidence shows. Brady, McLoughlin, Doan and Crockett combined two pre-registered observational studies of Twitter with two pre-registered experiments (Brady et al. 2021). The observational studies covered 7,331 users and 12.7 million tweets: 3,669 politically engaged users tweeting around the Kavanaugh hearings (6.1 million tweets) and 3,669 less political users tweeting around the United Airlines passenger-removal incident (6.6 million tweets). Outrage was measured with a classifier trained on 26,000 tweets that matched expert raters and was distinct from generic negative sentiment. The definition matters for this report: outrage is anger, disgust or contempt in response to a perceived moral violation, associated with blaming people, events or things, holding them responsible, or wanting to punish them. Outrage, as measured here, is blame expressed in public.

The core result is a reinforcement-learning signature. Outrage-specific feedback (likes and retweets) on one day predicted more outrage the next day (b = 0.03 in Study 1 and 0.02 in Study 2, both p < .001). In plain terms, a 100% increase in feedback yields an expected 2–3% increase in outrage expression the following day. The effect weakened with longer tweeting histories and responded to positive prediction errors, as learning models predict, and it was specific to outrage rather than generic negativity.

The second result is norm learning. Users embedded in more ideologically extreme networks expressed more outrage, controlling for their own ideology (b = 0.13 and 0.31). In the simulated-platform experiments (N = 120 each), participants exposed to an outrage norm chose outrage posts on the first trial with odds ratios of 4.94 and 5.38. Norms and reinforcement interacted: in more extreme networks, feedback mattered less, as if the norm had already been internalised. The authors spell out the long-run implication: norm and reinforcement learning “might encourage more moderate users to become less moderate over time”. Because expression can become decoupled from felt emotion, this “could result in a form of pluralistic ignorance … whereby people falsely believe their peers are more outraged than they actually are”.

The limits are real and the authors state them. The large studies are observational; network composition may be self-selected; the experiments used only liberal participants; and expression is not the same as feeling.

Stakes for trust. This is where the reward system becomes a training system. Platforms do not merely surface outrage that already exists; their feedback loops shape what users and elites learn to produce. Brady and colleagues put the governance point bluntly: newsfeed algorithms “can influence users’ moral behaviors by exploiting their natural tendencies for reinforcement learning”, and “crucial inputs to the learning process are shaped by corporate interests”. The 2–3% effect looks trivial for an ordinary user and is not trivial for a political leader with millions of followers, whose daily feedback can double many times over. The trust damage comes through misperception: if visible outrage outruns felt outrage, each side sees the other as angrier and more hostile than it is, and calibrates its own distrust accordingly.

What to watch. Visible reward counters on political content and whether platforms test hiding them · trends in outrage expression among high-follower political accounts relative to their felt or stated positions · meta-perception surveys measuring how hostile each side believes the other to be versus how hostile it is · whether the norm-cascade effect replicates outside the US and outside Twitter.

4. Platforms rewrite the economics of outrage

What the evidence shows. Crockett’s 2017 comment is a framework, not a large study, but it supplies the economic logic that the empirical work later tests (Crockett 2017). Outrage runs from stimuli to responses to outcomes, and digital media change all three. On the stimulus side, offline life is a poor supplier of moral violations: in the everyday-morality dataset Crockett reanalysed (1,252 North American adults signalled five times a day for three days), fewer than 5% of reported daily experiences involved directly witnessing or experiencing immoral acts. Online is different. Participants learned about immoral acts online more than in person or via traditional media, and acts encountered online evoked more outrage than those met in person or through print, TV or radio. Because platforms compete for attention, their algorithms promote what is most likely to be shared, “regardless of whether it benefits those who share it — or is even true”, and selection forces may favour “supernormal” stimuli.

On the cost side, expressing outrage online is cheap. Echo chambers reduce the risk of retaliation; people “hide in a crowd”; targets appear as “two-dimensional icons”. As Crockett puts it, “It’s a lot easier to shame an avatar than someone whose face you can see.” On the benefit side, outrage signals moral quality, and online it “instantly advertises your character to your entire social network and beyond”. Feedback arrives on an unpredictable reinforcement schedule, the classic recipe for habit, so that “a habitual online shamer might express outrage without actually feeling outraged”.

The MAD model of Brady, Crockett and Van Bavel generalises this into three channels — Motivation, Attention and Design (Brady, Crockett & Van Bavel 2020). Motivation: simply logging on can act as a group-identity manipulation, and moral emotions serve group-image goals — outrage and contempt derogate the out-group, while shame is “linked to blaming others, distancing oneself from the negative event, and denial”. Attention: moral and emotional words capture attention within about 250 milliseconds, and words with higher attentional capture predicted more retweets. Design: algorithms act as an “external attentional filter”; quantified likes and shares produce reinforcement; and fast response options favour quick emotional moral judgements. Scale amplifies all of it: online networks of 350–500 contacts on Facebook and about 700 on Twitter, against about 150 offline; and people are seven times more likely to share content when they perceive others are sharing it.

Crockett names the upside — online outrage lets “traditionally disempowered groups … check the behaviour of more powerful interests” — and the costs: messages “ricochet” inside echo chambers, the line between “the truly heinous” and “the merely disagreeable” blurs, and cheap expression substitutes for costlier action.

Stakes for trust. The MAD model identifies a mechanism that is easy to miss: outrage builds trust inside the group even as it destroys trust across groups. People who express outrage by punishing unfairness “are viewed as more trustworthy by others”, and moral-emotional displays raise status “as long as those reactions are well aligned with the value system of their social network” (Brady, Crockett & Van Bavel 2020, citing Jordan et al. 2016). That is the private return on public blame. The cost lands elsewhere — on intergroup trust, through dehumanisation and through the false polarisation that makes differences “appear worse online than they are in reality”. The substitution effect matters too: if online outrage replaces costly civic action, the accountability function that justifies outrage decays while its divisive function grows.

What to watch. The ratio of online outrage to offline costly action (donations, volunteering, complaints filed through formal channels) on salient issues · design features that lower the cost of expression — quote-posting, pile-on mechanics, anonymous replies — and whether friction experiments raise that cost · evidence on the proposed but largely untested interventions in the MAD model: hidden like counts, batched notifications, empathy prompts and superordinate identity cues.

5. The blame machine: who can blame whom, at what cost, with what reward

What the evidence shows. The library’s theory of blame comes from outside the social-media literature, and that is what makes it useful here. Malle, Guglielmo and Monroe define blame as a moral judgement that is “both cognitive and social, regulates social behavior, fundamentally relies on social cognition, and requires warrant” (Malle et al. 2014). Warrant is the key. One can say “it’s just wrong, I can’t tell you why”, but it is socially unacceptable to say “he deserves blame, but I can’t tell you why”. Because blame must be justified, “the community can fact-check the blamer’s warrant”. Ordinary blame, on this account, is largely evidence-tracking: in studies with notable outcome effects, information about mental states explained six times more variance in blame than outcomes did.

The machinery fails in a specific setting. Second-person blame — said to the offender’s face — aims at repair and works best with “low emotional intensity and high thoughtfulness”. Third-person blame, voiced to an audience without the offender present, “sometimes represents a first step toward socially excluding the offender”. Between strangers, “there is little pressure to communicate, persuade, repair, and find common ground”; moral criticism “becomes akin to road rage, an episode of Jerry Springer, or hateful anonymous comments on the internet”. The decisive line is that people “are far less likely to be called on for the lack of warrant in their judgments”. Malle and colleagues add that values can be “preset” by group membership — “the rival always intentionally harms us” — which is the formal hook for scapegoat-style blame.

Read against the platform evidence, these pieces assemble into a single mechanism. Social media converts a large share of blame from second-person to third-person, from known to stranger, and from private to performed. Three parameters change at once · Cost of blaming: falls — no face, low retaliation risk, a crowd to hide in (Crockett 2017) · Audience for the blamer: grows by an order of magnitude, from a handful of listeners to networks of hundreds or thousands (Brady, Crockett & Van Bavel 2020) · Reward: becomes visible, quantified and immediate, and is largest when the target is the out-group (Rathje et al. 2021; Brady et al. 2021). Meanwhile the brake — the demand for warrant — weakens, because the audience is self-selected for agreement and nobody asks the blamer to justify the charge.

This also changes who can blame whom. Before platforms, broadcasting blame to a mass audience required a newspaper, a broadcaster or a party apparatus, each with editorial and legal warrant checks. Now any account can address a mass audience, and any public figure, institution or group can be the target. That democratisation has a genuine upside — the disempowered can name the powerful (Crockett 2017) — but it also opens a channel for those Petersen and colleagues describe as chaos-seekers. Across eight US surveys (combined N = 10,921), a one-SD rise in “need for chaos” raised intentions to share hostile political rumours by about 10 percentage points; the motives were to mobilise against disliked groups and to amuse friends, while sharing because the story is true was negatively related (Petersen et al. 2023). The authors’ warning is that a few individuals with extraordinary negative sentiments “may still have a large reach”.

Stakes for trust. Blame that is cheap, mass-audienced and rewarded without warrant changes the trust equilibrium. Institutions face a constant stream of accusations whose cost to the accuser is close to zero and whose reach can exceed the institution’s own. Out-groups are cast as intentional agents of harm, because preset intentionality is the quickest path to a satisfying blame judgement. And blame shifts from a tool of repair to a tool of exclusion. Finkel and colleagues identify the narrative structure that results: “The other side cheats, so our side would be foolish to adhere to long-standing democratic norms” (Finkel et al. 2020).

What to watch. The share of political content that attributes generalised blame to a group — Ovadya and Thorburn list “attributes blame to a group using plural ‘you’ / ‘they’ pronouns” as an operational content label (Ovadya & Thorburn 2023) · whether platforms build “warrant” features — context, sourcing, notes rated helpful across divides — into blame-heavy content · the reach of high-grievance minorities relative to their size · whether institutions under attack respond with second-person, repair-oriented communication or with counter-blame.

6. Moral contagion is real — but smaller than first thought

What the evidence shows. The claim that moral-emotional language spreads further became one of the founding results of the field. The original 2017 study, as summarised in the MAD model, found that each moral-emotional word added to a tweet was associated with a roughly 20% increase in sharing, and Donald Trump gained an expected 27% more retweets per moral-emotional word against 2% for Hillary Clinton (Brady, Crockett & Van Bavel 2020). Brady, Rathje, Globig and Van Bavel then did what too few fields do: they tested their own result (Brady et al. 2025). A pre-registered direct replication used 849,266 tweets on five new US topics, and a meta-analysis pooled 27 studies from five labs with a combined N of 4,821,006.

The meta-analytic estimate is an incidence rate ratio of 1.13 (95% CI 1.06–1.20): each additional moral-emotional word is associated with 13% more shares, not 20%. The replication varied by topic · Kavanaugh: 1.05 · border-wall shutdown: 1.07 · transgender military ban: 1.66 · COVID-19 masks: 1.12 · George Floyd: 0.97, a reversal. The combination matters: distinctly emotional words alone had a mean IRR of 1.00 and distinctly moral words 1.03; it is moral and emotional language together that predicts sharing. The robustness checks are reassuring — no detectable publication bias (Egger’s test P = 0.171) and evidential value on the p-curve — but the measurement caveat is serious: dictionary and LLM measures of moral-emotional content correlate at only r = 0.27. The authors are explicit that the analysis “cannot establish a causal link”.

Stakes for trust. This mechanism is ranked here for a reason: it calibrates the others. Moral contagion is real, robust and modest, and its sign depends on context. It is dwarfed by the out-group effect — Rathje’s per-word out-group premium of 67% against a moral-emotional premium of about 10% in the same data — which suggests that part of what looked like contagion of moral emotion is contagion of blame aimed at the other side. The George Floyd reversal, and the observation that positive emotions spread most after the 2015 same-sex marriage ruling (Brady, Crockett & Van Bavel 2020), show that online moral emotion is not inherently corrosive. Outrage spreads most where derogating the out-group is already the norm. That matters for policy: it argues against treating emotional or moral language as the target, and for targeting the out-group-hostile, blame-laden subset.

What to watch. Whether future work separates out-group reference from moral-emotional language in the same models · contexts in which moral emotion is shared positively, and what distinguishes them · measurement convergence between dictionary, LLM and human coding, since the size of the problem currently depends on the instrument used to measure it.