Exposure to Marginally Abusive Content on Twitter
Open accessAbstract
Social media platforms can help people find connection and entertainment, but they can also show potentially abusive content such as insults and targeted cursing. While platforms do remove some abusive content for rule violation, some is considered "margin content" that does not violate any rules and thus stays on the platform. This paper presents a focused analysis of exposure to such content on Twitter, asking (RQ1) how exposure to marginally abusive content varies across Twitter users, and (RQ2) how algorithmically-ranked timelines impact exposure to marginally abusive content. Based on one month of impression data from November 2021, descriptive analyses (RQ1) show significant variation in exposure, with more active users experiencing higher rates and higher volumes of marginal impressions. Experimental analyses (RQ2) show that users with algorithmically-ranked timelines experience slightly lower rates of marginal impressions. However, they tend to register more total impression activity and thus experience a higher cumulative volume of marginal impressions. The paper concludes by discussing implications of the observed concentration, the multifaceted impact of algorithmically-ranked timelines, and potential directions for future work.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The engagement-based algorithm causes users to spend more time on the platform, relative to the reverse-chronological timeline.
“This is notable because, as measured by internal A/B tests at Twitter, the engagement-based algorithm causes users to spend more time on the platform, relative to the reverse-chronological timeline [7,37].”
From Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023) · cites this paper as [7]
The Algorithmic Impact (RQ2) section describes an experimental comparison of algorithmically ranked and reverse-chronological timelines. The results passage under Figure 1 reports more tweet impressions and overall activity for accounts with algorithmic timelines. It does not report time spent on the platform, so the claimed increase in time is not established by this source. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
How social learning amplifies moral outrage expression in online social networks
Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021) · Science Advances · doi:10.1126/sciadv.abe5641 · Cited by 375 · see citing works
Reporting, Reviewing, and Responding to Harassment on Twitter
Matias, J. N., Johnson, A., Boesel, W. E., Keegan, B., Friedman, J., & DeTar, C. (2015) · arXiv · doi:10.48550/arxiv.1505.03359 · Cited by 48 · see citing works
Twitter manipulates your feed: Ethical considerations
Fiske, S. T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119924119 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 22
- Alexander, J. 2019. YouTube claims its crackdown on bor- derline content is actually working. The Verge. Amnesty International. 2018. Toxic Twitter: A Toxic Place for Women. https://www.amnesty.org/en/latest/research/ 2018/03/online-violence-against-women-chapter-1-1/. Ac- cessed: 2023-04-19.
- Bruns, A. 2019. Are filter bubbles real? John Wiley & Sons.
- Chandrasekharan, E.; Samory, M.; Srinivasan, A.; and Gilbert, E. 2017. The bag of communities: Identifying abu- sive behavior online with preexisting internet data. In Pro- ceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3175-3187.
- Davidson, T.; Warmsley, D.; Macy, M.; and Weber, I. 2017. Automated hate speech detection and the problem of offen- sive language. In Proceedings of the International AAAI Conference on Web and Social Media, volume 11.
- Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018. Bert: Pre-training of deep bidirectional transformers for lan- guage understanding. arXiv preprint arXiv:1810.04805. · arXiv
- Farris, F. A. 2010. The Gini index and measures of inequal- ity. The American Mathematical Monthly, 117(10): 851- 864.
- Feuston, J. L.; Taylor, A. S.; and Piper, A. M. 2020. Con- formity of Eating Disorders through Content Moderation. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1).
- Gillespie, T. 2018. Custodians of the Internet. Yale Univer- sity Press.
- Huszár, F.; Ktena, S. I.; O'Brien, C.; Belli, L.; Schlaikjer, A.; and Hardt, M. 2022. Algorithmic amplification of politics on Twitter. Proceedings of the National Academy of Sciences, 119(1).
- Katsaros, M.; Yang, K.; and Fratamico, L. 2021. Reconsid- ering Tweets: Intervening During Tweet Creation Decreases Offensive Content. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM).
- Kwak, H.; Blackburn, J.; and Han, S. 2015. Exploring cyber- bullying and other toxic behavior in team competition online games. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (CHI).
- Lorenz, M. O. 1905. Methods of measuring the concen- tration of wealth. Publications of the American statistical association, 9(70): 209-219.
- Malik, M. M.; and Pfeffer, J. 2016. Identifying platform effects in social media data. In Tenth International AAAI Conference on Web and Social Media.
- Munger, K.; and Phillips, J. 2022. Right-wing YouTube: a supply and demand perspective. The International Journal of Press/Politics, 27(1).
- Nagar, Y. 2012. What do you think? The structuring of an online community as a collective-sensemaking process. In Proceedings of the ACM 2012 conference on computer sup- ported cooperative work.
- Nurik, C. 2019. "Men are scum": Self-regulation, hate speech, and gender-based censorship on Facebook. Inter- national Journal of Communication, 13: 21.
- Orellana-Rodriguez, C.; and Keane, M. T. 2018. Attention to news and its dissemination on Twitter: A survey. Computer Science Review, 29.
- Redmiles, E. M.; Bodford, J.; and Blackwell, L. 2019. "I just want to feel safe": A Diary Study of Safety Perceptions on Social Media. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM).
- Roberts, S. T. 2019. Behind the screen. Yale University Press.
- Scheuerman, M. K.; Jiang, J. A.; Fiesler, C.; and Brubaker, J. R. 2021. A Framework of Severity for Harmful Content Online. Proceedings of the ACM on Human-Computer In- teraction, 5(CSCW2).
- Starbird, K.; Arif, A.; and Wilson, T. 2019. Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW).
- Vitak, J.; Chadha, K.; Steiner, L.; and Ashktorab, Z. 2017. Identifying women's experiences with and strategies for mit- igating negative effects of online harassment. In Proceed- ings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW).
Cited by
Sorted by citations
Milli, S., Carroll, M., Wang, Y., Pandey, S., & Dragan, A. D. (2023) · arXiv.org · doi:10.48550/arxiv.2305.16941 · Cited by 200 · see citing works
Echo Chambers in the Age of Algorithms: An Audit of Twitter’s Friend Recommender System
Duskin, K., Schafer, J. S., West, J. D., & Spiro, E. S. (2024) · ACM Web Science Conference · doi:10.1145/3614419.3643996 · Cited by 17 · see citing works
- Authors
- Bandy, J., & Lazovich, T.
- Affiliations
- Northwestern University (US)
- Northeastern University (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2023-06-02
- Volume
- 17
- Pages
- 24-33
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W4380363214
- Cited by
- 2
- References
- 3
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.28
- Type
- conference paper
- Language
- en
- Open access
- diamond
- dx.doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- Twitter, online harassment, insults, content exposure, user activity, content moderation, social media abuse
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
