Funhouse Mirror Factory: Social Media Distorting Norms

Introduction

The internet has experienced one of the fastest technological revolutions in history [1].
Approximately 5 billion people worldwide use social media, spending an average of two and a half hours daily online [2].
Social media is likened to a funhouse mirror, distorting our collective sense of what is normative [3].
Online discussions are dominated by a small, vocal, and non-representative minority.
Only 3% of active accounts are toxic but produce 33% of all content [4].
74% of online conflicts start in just 1% of communities [5].
0.1% of users share 80% of fake news [6, 7].
This extreme minority stirs discontent, spreads misinformation, and biases the meta-perceptions of passive users, leading to false polarization and pluralistic ignorance.
Exposure to extreme content can normalize unhealthy and dangerous behavior.
- Teens exposed to extreme content related to alcohol consumption thought dangerous alcohol consumption was normative [12].
The paper aims to explain:
- How online environments become saturated with false norms.
- Who is misrepresented online.
- What happens when online norms deviate from offline norms.
- Where people are affected online.
- Why expressions are more extreme online.
The goal is to provide a framework for understanding and correcting distortions in social perceptions created by social media platforms.

How Norms Become Distorted Online

Social norms are defined as the “predominant behaviors, attitudes, beliefs, and codes of conduct of a group” [13].
Norms are tied to people’s social identity, signaling group affiliation and strengthening ties [14].
New group members quickly learn and follow new norms, which are relatively impervious to reinforcement learning [15, 16].
Conforming to norms demonstrates commitment to the group [17–19].
When a norm is ambiguous, people base decisions on the group consensus [20].
Accurately detecting social norms is critical for social acceptance.
People form an average representation of a series of exemplars in a group via ensemble encoding [21, 22].
Ensemble coding is cognitively efficient, encoding a single representation of a set of stimuli rather than memorizing every item [21].
Socially, ensemble coding allows people to form a single estimation of group emotion or opinion [23, 24].
People encode social norms from posts and comments in online forums and social media platforms.

Whose Opinions Are Represented Online?

Ensemble coding can be distorted online due to the structure of normative information.
False norms emerge because social media is dominated by a small number of extreme people who post their most extreme opinions at a very high volume [25–27].
Moderate or neutral opinions are practically invisible online.
Encountering a disproportionate volume of extreme opinions can lead to false perceptions that norms are far more extreme than they actually are.
Online consumer reviews often reflect extremely positive or negative experiences [28].
On platforms like Instagram, there is a norm to present oneself as interesting, attractive, and successful [29].
On LinkedIn, people disproportionately report successes and accomplishments rather than failures [30].
In online political discussions, the people who post frequently are often the most ideologically extreme [31, 32].
- 97% of political posts from Twitter/X come from just 10% of the most active users [33].
- Most people are ideologically moderate, uninterested in politics, and avoid political discussions offline [34–36].
- In discussions of the Covid-19 vaccine on Twitter, only 0.35% of people were in true echo chambers, yet those users dominated the overall discourse [37].
- An analysis of social media found that a third of low-credibility posts were shared by just 10 accounts [26, 38].
Moderate opinions are effectively invisible on social media.
Biased inputs from the online environment can lead to extremely biased outputs when people rely on ensemble coding.
This is especially problematic for topics like politics, where opinions are invisible, and people are generally hesitant to share their opinions in everyday life.
People base their perception of norms on an unrepresentative sample of opinions and images, leading to a distorted view of social norms.
People tend to weight extreme content more heavily when taking the average of a set of stimuli [23] and assume greater moral outrage from a post than the authors report feeling [39].
The conceptualized average of opinions for one’s ingroup and outgroup in the online world can be far more extreme than true offline norms.

What Norms Dominate Online Discourse?

Online discourse is dominated by the most extreme people, and negativity, intergroup hostility, and polarization appear strikingly prevalent online [40–43].
The most widely shared content on Twitter/X and Facebook is moralized content [40, 42, 44].
The norms generated on some social media platforms might be more hostile than in the offline world.
Social media feeds are curated to normalize extreme norms, fostering a false reality [46].
Even if people recognize that certain visible behaviors do not reflect how people actually are, they are still reinforced through “likes.”
Extreme norms can leave people feeling inadequate.
- Constant exposure to extreme outliers in body shape on platforms like Instagram may contribute to lower body image and depression in teen girls [47].
Early evidence suggests that spillover does occur in some domains.
- Online content that normalizes alcohol consumption can lead college students to overestimate how common drinking is offline [48–50].
More research is needed on the impact of online content on perceptions of real-world norms [51].
These norms contradict social media users’ expressed preferences [52].
Even though incivility from politicians is increasing and is increasingly socially rewarded online [53], people report that they want to hear less from uncivil politicians [54].

Why Are False Norms Worse Online Than Offline?

Social media operates in an attention economy, where design features and algorithms are designed to elicit as much engagement as possible [3, 55].
There is a strong incentive for users to create content that captures attention and maximizes engagement rather than content that reflects reality.
Users who are the most active on social media are also the most extreme [25, 32].
News stories that express outgroup animosity are 67% more likely to be shared on social media [42].
People with more extreme or hostile beliefs tend to dominate discourse, leading to false beliefs about the norms of a community [31].
Online dynamics are amplified by design features and recommendation algorithms on various platforms [56, 57].
A recent analysis of the algorithm on Twitter/X found that it prioritizes evocative content [44].
People focused on gaining social status are the most hostile online [25].
There is often little motivation for someone to post a nuanced or moderate opinion on social media.
Nuanced or moderate posts often risk hostility from more extreme ingroup and outgroup members [58].
People who are politically moderate were more likely to report being harassed online, even though they were also less likely to post [31].
People who “troll” others typically have higher dark triad characteristics [59].

Conclusion

Users form beliefs about the state of the world based on the most extreme voices as they scroll content.
Being overexposed to the most extreme opinions from the most extreme people can have real consequences.
Believing that one’s political outgroup endorses extreme political positions may lead to biased meta-perceptions, pluralistic ignorance, and false polarization [9, 10, 39, 61].
It is challenging to differentiate what is normative vs. unpopular when the content that drives the most engagement is often from a minority of extreme users.
Misperceptions might be driven by factual content, making them uniquely difficult to address through content moderation.
People may develop a distorted sense of reality as we rely on the funhouse mirror to reflect the truth.

Credit author statement

Claire E. Robertson: Conceptualization, Writing – Original Draft, Writing – Review and Editing, Visualization.
Kareena S. Del Rosario: Conceptualization, Writing – Original Draft, Writing – Review and Editing.
Jay J. Van Bavel: Conceptualization, Writing – Original Draft, Writing – Review and Editing.

Declaration of competing interest

Jay Van Bavel reports financial support was provided by Google Jigsaw.
Jay Van Bavel reports financial support was provided by John Templeton World Charity Foundation (TWCF-2022-30561).

Data availability

No data was used for the research described in the article.