The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms
Abstract
Social media platforms play an increasingly important civic role as platforms for discourse, where we discuss, debate, and share information. This article explores how users make sense of the content moderation systems social media platforms use to curate this discourse. Through a survey of users ( n = 519) who have experienced content moderation, I explore users’ folk theories of how content moderation systems work, how they shape the affective relationship between users and platforms, and the steps users take to assert their agency by seeking redress. I find significant impacts of content moderation that go far beyond the questions of freedom of expression that have thus far dominated the debate. Raising questions about what content moderation systems are designed to accomplish, I conclude by conceptualizing an educational, rather than punitive, model for content moderation systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gillespie, T. (2010) · New Media & Society · doi:10.1177/1461444809342738 · Cited by 2,522 · cited 2×
“The term platform itself can serve as an obfuscating device, operationalized at times by social media companies in order to divert tensions between their obligations to multiple constituencies (Gillespie, 2010).”
Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed
Rader, E., & Gray, R. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702174 · Cited by 386 · cited 1×
“These theories are often incomplete, are generated out of each user’s personal experiences, and serve to help them both to filter information and to make decisions about how they interact with the world (Gelman and Legare, 2011; Jones et al., 2011; Rader and Gray, 2015).”
Cited by
(2019) · doi:10.1080/1369118x.2019.1573914 · Cited by 719
Managing visibility on YouTube through algorithmic gossip
Bishop, S. (2019) · New Media & Society · doi:10.1177/1461444819854731 · Cited by 535
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373
Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator
Jhaver, S., Birman, I., Gilbert, E., & Bruckman, A. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3338243 · Cited by 289
Misinformation, Disinformation, and Online Propaganda
Guess, A. M., & Lyons, B. A. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.003 · Cited by 284
Metadata
- Authors
- Myers West, S.
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2018-05-08
- Volume
- 20
- Issue
- 11
- Pages
- 4366-4383
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W2800596622
- Cited by
- 477
- References
- 2
- FWCI
- 299.22
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
