Like Walking a Tightrope
User-Centric Perspectives on Content in Need of Moderation
Bibliographic Data
| ID | 24217864 |
|---|---|
| Authors | Anna Maria Planitzer (0000-0001-8181-6615, University of Vienna), Svenja Schäfer (0000-0002-6401-1564, Wageningen University & Research, The Netherlands), Pia Pachinger (0009-0007-7490-0422, TU Wien), Sophie Lecheler (0000-0001-7320-1012, University of Vienna) |
| Year | 2026 |
| Volume | 12 |
| Issue | 3 |
| Publication date | 2026-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Media + Society (JOURNAL) |
| Journal identifiers | ISSN: 2056-3051 • E-ISSN: 2056-3051 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/20563051261479963 |
| OpenAlex | W7214551496 |
| Language | EN |
| References cited | 56 |
Norm-violating content on social media platforms is on the rise, fueling prejudice and polarization. To counter such developments in a timely manner, platforms rely on automated content moderation. The issue, however, is that current understandings of automated moderation are mostly elite-driven, neglecting individual user perspectives, introducing biases and discrimination, and overlooking that users’ perspectives on what constitutes a norm violation in the first place may differ substantially from those of experts and platforms. Therefore, this study uses focus groups to explore what type of content users themselves think is in need of moderation. Findings suggest that users, while acknowledging the importance of moderating polarized topics (e.g., migration) and uncivil discourse styles (e.g., insults), are more concerned with the motivations of perpetrators and the vulnerability of targets. Our findings show that, if platforms want to incorporate user perspectives when designing automated moderation systems, they must move beyond merely detecting problematic message characteristics and focus on more tailored approaches, alongside accountability measures for perpetrators and safety measures for victims.
automated content moderation · insults · Migration · perpetrator accountability · platform design · Prejudice · social media polarization · uncivil discourse · user perspective · victim safety · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Social Media and Politics
Qualitative Text Analysis
The WWW of digital hate perpetration
Factors related to user perceptions of artificial intelligence (AI)-based content moderation on social media
Perceptions and Evaluations of Incivility in Public Online Discussions—Insights From Focus Groups With Different Online Actors
Verbal Venting in the Social Web
What is a flag for? Social media reporting tools and the vocabulary of complaint
Your social ties, your personal public sphere, your responsibility
Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation
Discourse architecture, ideology, and democratic norms in online political discussion
Censored, suspended, shadowbanned
The influence of the deliberative quality of user comments on the number and quality of their reply comments
Common sense or censorship
How social media users perceive different forms of online hate speech
When AI moderates online content
Can AI Enhance People’s Support for Online Moderation and Their Openness to Dissimilar Political Views
Differential perceptions of and reactions to incivil and intolerant user comments
Different platforms, different uses
Platform governance at the margins
An autoethnography of automated powerlessness
Citizen science and Post-Normal Science’s extended peer community
Weathering and weaponizing the #TwitterPurge
Towards A Typology of Conflict Frames
Democracy online
What to Believe? Social Media Commentary and Belief in Misinformation
Navigating the gray areas of content moderation
Do users want platform moderation or individual control? Examining the role of third-person effects and free speech support in shaping moderation preferences
Algorithmic content moderation
Ethical scaling for content moderation
Content moderation, AI, and the question of scale
Shall AI moderators be made visible? Perception of accountability and trust in moderation systems on social media platforms
Toxic Speech and Limited Demand for Content Moderation on Social Media
The Dynamics of Political Incivility on Twitter
You Can (Not) Say What You Want
Do Not Recommend? Reduction as a Form of Content Moderation
Searching for the Dimensions of Today's Political Incivility
| Highly cited | No |
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