Scrolling and Shifting: Social Media's Influence on Attitudes, Bias, and Discrimination

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Scrolling and Shifting: Social Media's Influence on Attitudes, Bias, and Discrimination

Advanced Social Psychology · Chapter 12: Social Psychology in the Digital AgeSection 12.4 · lesson 82 of 8510 min · free with a free account

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In this lesson

What happens to attitudes when the information environment is optimized against you.

  • Assess the empirical strength of filter bubbles
  • Apply social comparison theory to image-centric feeds
  • Trace 3 pathways from platform design to discrimination
  • Evaluate mitigation at individual, platform, and structural levels

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Transcript

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Social Psychology in the Digital Age

Scrolling and Shifting

Attitudes are formed by what you are exposed to. Which was, for most of the history of this field, a fairly boring observation. It stopped being boring when exposure became personalized. This section is about what happens to social psychology's classical findings. When the information environment is optimized in real time against each user. We will look at filter bubbles, and at how strong the evidence actually is. At group polarization, which long predates the internet. At social comparison, which predates it by even longer. And at three distinct pathways from platform design to discrimination. The mitigation section at the end is unusually concrete.

A Broken Assumption

Classical Attitude Research Assumed a Stable Environment

Attitudes are evaluative orientations toward people, objects, and ideas. They are formed and revised through exposure, persuasion, and social influence. Classical attitude research made one assumption that no longer holds. Dual-process accounts like the Elaboration Likelihood Model are the anchor here. Petty and Cacioppo published it in nineteen eighty-six. Those models treated the information environment as a relatively stable input. Something the researcher could hold constant while varying the message. Algorithmic personalization has rendered that assumption obsolete. What a user encounters is dynamically tailored to inferred preferences. Which shifts the baseline against which attitude change is measured. In experimental terms, the independent variable is no longer independent.

Three Features

What Makes a Curated Environment Different

Three interacting features distinguish algorithmically curated environments. The first is that exposure is selective at scale. Not merely as a function of individual choice. But as a function of system-level optimization. The second is that feedback is continuous. Every interaction updates the model of the user. Which tightens the alignment between content and prior preference. The third feature is the one that belongs most directly to this course. Likes, shares, and reaction counts act as peripheral persuasion signals. They shape attitudes through normative routes rather than informational ones. A reaction count tells you nothing about whether a claim is true. It tells you what other people appear to think, which is a very old lever.

Two Constructs

Filter Bubbles and Echo Chambers Are Not the Same

Two constructs get used interchangeably, and they should not be. The filter bubble hypothesis comes from Pariser, in twenty eleven. Personalization algorithms restrict the diversity of information a user encounters. The result is informational isolation. The echo chamber is related but distinct. It describes a homogeneous social environment. One in which the same views circulate repeatedly, with little dissent. Notice the difference in what each one is about. Algorithmic narrowing shapes the inputs you see. User-driven network selection shapes who discusses those inputs with you. The two compound, which is why fixing one leaves the other running.

The Loop

Outputs Reinforcing the Preferences They Inferred

The mechanism is a feedback loop, and it is worth tracing carefully. The system infers a preference from something you did. It tailors content to match that inferred preference. You engage with the tailored content, because it was chosen to be engaging. That engagement updates the model of you. And the alignment between content and preference tightens. Then the loop runs again, from a narrower starting point. Notice what closes the loop. It is your own behavior, which is why the system can claim to be neutral. It is showing you what you demonstrably wanted. But what you wanted was itself measured inside the loop. That circularity is the whole difficulty.

The Dispute

How Strong Is the Bubble, Really?

How strong is the bubble effect really? Estimates vary considerably. And the disagreement is instructive, so it is worth knowing in detail. Bakshy, Messing and Adamic published a challenge in twenty fifteen. On at least one major platform, user choices mattered more than algorithms. Bruns argued in twenty nineteen that the bubble metaphor overstates isolation. The contemporary consensus lands somewhere in between. Bubbles are real, but they are heterogeneous. They operate more strongly on highly engaged users. More strongly on contested topics. And more strongly where chronological feeds have been suppressed. Note the shape of that mature finding. Not does it exist, but for whom, on what, and under which design.

Polarization

Two Mechanisms That Predate the Network

Group polarization long predates networked communication. It is the tendency of group discussion to push members toward extremes. More extreme versions of their pre-existing inclinations, specifically. Myers and Lamm demonstrated it experimentally in nineteen seventy-six. Two mechanisms drive the effect, and they are different in kind. The first is persuasive arguments. Members encounter new reasons consistent with their initial leaning. That is an informational mechanism. The second is social comparison. Members adjust toward the perceived group norm, to gain favorable evaluation. That is a normative mechanism. One moves you because you learned something. The other, because you want regard.

Potentiated Online

Every Mechanism Gets Stronger

Online environments potentiate both mechanisms at once. Selective exposure concentrates persuasive arguments on one side of an issue. Visible engagement metrics intensify the perceived group norm. And SIDE-model dynamics substitute group identity for personal identity. Which amplifies conformity to ingroup positions. Yardi and boyd studied this on Twitter discussion threads, in twenty ten. They found measurable polarization within hours of contentious events. And one finding in particular deserves attention. Cross-cutting exchange yielded hostility rather than moderation. Think about what that does to the intuitive remedy. The obvious fix is to expose people to the other side. On this evidence, contact without structure does not moderate. It hardens.

Festinger, 1954

How People Evaluate Themselves

Festinger proposed social comparison theory in nineteen fifty-four. In the absence of objective standards, people evaluate themselves by comparison. Later work split that into two directions. Upward comparison is with better-off targets. It often yields dejection, though it can also yield motivation. The outcome is genuinely not fixed. Downward comparison is with worse-off targets. It is typically protective of self-esteem. Now read the condition built into Festinger's theory. Comparison does the evaluating when objective standards are absent. And most questions about how your life is going have no objective standard.

By Design

Feeds Saturated With Upward Targets

Image-centric platforms saturate users with upward comparison targets. Curated, edited, exceptional content occupies most of the feed. Yoon, Kleinman, Mertz and Brannick did the meta-analysis in twenty nineteen. Upward social comparison on these sites is linked to depressive symptoms. With small-to-moderate effect sizes. But the interesting part is the mediator and the moderator. The mediator is envy. The comparison does not act directly; it acts through an emotion. The moderator is self-esteem. The same feed does not do the same thing to two different people. The mediator tells you where to intervene. The moderator tells you for whom the intervention is urgent.

Discrimination

Three Pathways, and What They Produce

Online environments do not merely reflect existing prejudice. They actively reshape how it is expressed, and where it lands. Three pathways are well documented. The first is disinhibition, which lowers the cost of expressing prejudice. It transforms private bias into public utterance. The second is algorithmic amplification. Engagement-optimized ranking rewards emotionally charged content. Including outgroup derogation, which is reliably charged. The third is training-data bias. Patterns encoded from historical content propagate into automated systems. Systems that mediate access to information, employment, and credit. The documented forms of harm range from microaggressions to coordinated harassment. And they differ in consequence, from chronic stress to withdrawal from public life.

Consolidate

Mitigation Has to Work at Three Levels

Mitigation strategies cluster at three levels, and all three are needed. At the individual level: perspective-taking training. And exposure to counter-stereotypic exemplars. At the platform level, the first tool is friction insertion. A delay before posting, or a prompt for civility. Plus transparency about ranking criteria. And diversification of recommended content. At the structural level: regulation, algorithmic auditing, and community moderation. Now here is why you cannot pick just one level. An intervention aimed at interpersonal discrimination does nothing about training-data bias. And an audit of a ranking system does nothing about a hostile thread. The pathways are independent, so the remedies have to be as well.

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FAQ

Questions students ask us

About Scrolling and Shifting: Social Media's Influence on Attitudes, Bias, and Discrimination

What is the difference between a filter bubble and an echo chamber?
A filter bubble is informational isolation created by personalization algorithms that narrow what a user sees, a term coined by Eli Pariser in 2011. An echo chamber is a social environment where like-minded people repeat the same views with little dissent, shaped mostly by whom users choose to follow. One concerns what the algorithm shows you; the other concerns who discusses it with you. The two often reinforce each other.
Are filter bubbles real?
Filter bubbles exist, but their effects appear weaker and more uneven than the popular idea suggests. A 2015 Facebook study by Eytan Bakshy, Solomon Messing, and Lada Adamic found that users' own choices limited exposure to opposing views more than the ranking algorithm did, and Axel Bruns argued in 2019 that the metaphor overstates isolation. Effects appear stronger for highly engaged users and on contested topics.
What causes group polarization?
Group polarization, the tendency of group discussion to make members' views more extreme in the direction they already leaned, has two main causes, reviewed by David Myers and Helmut Lamm in 1976. With persuasive arguments, members hear new reasons supporting their initial view. With social comparison, members shift toward the position the group seems to value. Online, one-sided feeds and visible likes can strengthen both mechanisms.
How does social comparison on social media affect mental health?
Frequent upward comparison with people who seem better off is linked to depressive symptoms, and image-heavy feeds supply many such comparisons. Leon Festinger's 1954 social comparison theory holds that people judge themselves against others when objective standards are missing. A 2019 meta-analysis by Yoon and colleagues linked upward comparison on social networking sites to depression. Research suggests envy is one route, and effects differ from person to person.

About the Advanced Social Psychology course

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Advanced Social Psychology (Psychology 310) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
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