“Social media algorithms are intentionally designed to amplify outrage and contribute to the spread of cancel culture.”

Social media newsfeed algorithms can directly affect how much social feedback a given post receives by determining how many other users are exposed to that post. Because we show here that social feedback affects users’ outrage expressions over time, this suggests that newsfeed algorithms can influence users’ moral behaviors by exploiting their natural tendencies for reinforcement learning... Such norm learning processes, combined with social reinforcement learning, might encourage more moderate users to become less moderate over time.

Evidence for algorithms driving or reinforcing unhealthy dynamics is very thin, supporting, at best, a small effect on mood. Furthermore, algorithms that emphasize nuanced content could help decrease paralyzing climate anxieties or highlight constructive perspectives that motivate action. However, direct evidence supporting these conclusions remains scarce.

Cancel culture, operating through social media denouncements, not only shapes public discourse but also amplifies false narratives, contributing to the dissemination of misinformation. The canceling phenomenon, often driven by genuine concerns for justice and accountability, can inadvertently lead to the silencing of diverse perspectives and a potential erosion of democratic values. This is directly related to the dynamics of the cancel culture: the new narrative imposed as politically correct penetrates all areas and establishes the rules of the game of the new cultural wars conducted with the cancelation of inconvenient and non-aligned content.

Our study reveals that the engagement-based algorithm tends to amplify emotionally charged content, particularly that which expresses anger and out-group animosity... Of the political tweets chosen by Twitter’s algorithm, 62 percent expressed anger and 46 percent contained out-group animosity... Our randomized experiment provides causal evidence that, indeed, the algorithm amplifies emotionally-charged content.

Here, we show how social learning processes amplify online moral outrage expressions over time... positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning... Social reinforcement and norm learning interact with social media design to amplify moral outrage in online social networks.

This research suggests that — in addition to amplification in emotion production — there is a complementary process by which perceivers interpret moral outrage. Social media algorithms amplify emotional content, including moral outrage, through engagement optimization.

Less talked about is the way algorithms actually perpetuate cancel culture. Algorithms love outrage. My own research has shown how content that sparks an intense emotional response —positive or negative — is more likely to go viral. Outrage is the perfect negative emotion to attract attention and engagement — and algorithms are primed to pounce.

Social media's business model of personalized virality is incompatible with democracy, agreed experts at a recent Harvard Law School discussion. Social media, he said, erodes both education and social solidarity. “[It] can’t not polarize the population. No matter where you stand — if masks are your thing, or vaccines, or critical race theory — it doubles down on your perspective or reminds you why the other side is wrong."

If algorithms are trained on unrepresented, incomplete, or skewed data, it can lead to automation bias against certain groups regarding their ethnicity, political affiliation, sexual preference, gender, or race. For example, algorithms might recommend or amplify divisive content that reinforces racial stereotypes, ultimately perpetuating historical inequities. Conversely, algorithms may disproportionately screen and suppress content that challenges cultural norms, which may reinforce prejudiced viewpoints.

New research hijacks social media platform rankings to study how great an impact the algorithm has on political polarization. The browser extension uses a large language model (LLM) to classify and rerank posts that appear on the subjects’ X feeds according to, as they call it, “antidemocratic attitudes and partisan animosity.”

A new Tulane University study explains why politically charged content gets more engagement from those who disagree... This shows how outrage drives engagement, which algorithms optimize for, amplifying such content.

If you ever feel as if social media apps are pushing enraging content to you, that's because they are. Social media platforms are designed to maximize engagement, and studies show that outrage is one of the most efficient strategies for keeping users engaged. “(Social media platforms) notice that more individuals are becoming engaged in either sensationalized content or content that makes people angry. And so they then amp up that content.”

New research published in Science and Nature suggest that Facebook and Instagram are not causing political polarization. It reportedly found that the algorithms increased user engagement but didn't affect people's existing political attitudes or polarization. However, the behavioral data show that the algorithms increased exposure to uncivil content and removed political content, particularly content from moderate friends and ideologically mixed audiences.

Because canceling shuts down the opportunity to learn and grow from mistakes, this makes the “canceled” more prone to becoming a target for hate ...

In practice, though, every piece of content a user encounters has already been filtered, ranked and shaped by algorithms designed primarily to maximize engagement on the platform. The most important first step, he argues, is recognizing that what appears in a feed is the result of deliberate design, not a neutral window onto the world — and that understanding how these systems work is ultimately what gives users the agency to make more informed choices about where and how they participate online.

The “Facebook Papers” exposé in October 2021 alleges the social media giant was aware its algorithm changes were making users angrier, and might even have been inciting violence. The algorithm change was designed to decrease disengagement by prioritising comments rather than just scrolling or a “like”. The problem is that it's divisive content that's most likely to attract comments. The algorithm “preys on the most primal parts of your brain [and] triggers your strongest emotions”.

In the era of social media, cancel culture has spread widely and affected people from all walks of life. Its psychological impacts are significant and include ... This creates a permanently open exposure where one’s behavior and words may be put under public comment regardless of the motives. A new behavioral pattern called “always online,” which is characterized by the expectation of being constantly connected to social networks, further aggravates the psychological pressure on individuals.

YouTube's 2019 internal changes to its recommendation algorithm explicitly aimed to reduce 'borderline content' that promotes outrage and sensationalism, indicating prior design prioritized engagement over harm reduction. This was in response to criticisms that algorithms amplified divisive content for profit.

Regardless of your perspective, the ripple effects of cancel culture are undeniable. Careers have been derailed, companies have lost significant market value overnight, and brands have scrambled to distance themselves from controversy—all stemming from viral moments online.