Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers
Open accessAbstract
With the rise in user generated content, there is a greater need for content reviews. While machines and technology play a critical role in content moderation, the need for manual reviews still remains. It is known that such manual reviews could be emotionally challenging. We test the effects of simple interventions like grayscaling and blurring to reduce the emotional impact of such reviews. We demonstrate this by bringing in interventions in a live content review setup thus allowing us to maximize external validity. We use a pre-test post-test experiment design and measure review quality, average handling time and emotional affect using the PANAS scale. We find that simple grayscale transformations can provide an easy to implement and use solution that can significantly change the emotional impact of content reviews. We observe, however, that a full blur intervention can be challenging to reviewers.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Some studies evaluating technological interventions such as image modification through blurring and filters found positive effects like reduced negative feelings while retaining operational efficiency.
“On one hand some studies (13–15), evaluating technological interventions such as image modification through blurring and filters, found positive effects like reduced negative feelings while retaining operational efficiency.”
From Longitudinal comparison of psychological outcomes of professional content moderators engaged in resilience training program (2025) · cites this paper as [14]
Cites
Development and validation of brief measures of positive and negative affect: The PANAS scales.
Watson, D., Clark, L. A., & Tellegen, A. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.6.1063 · Cited by 32,433 · see citing works
A Survey on Hate Speech Detection using Natural Language Processing
Schmidt, A. G., & Wiegand, M. (2017) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/w17-1101 · Cited by 1,447 · see citing works
Pekrun, R. (1992) · Applied Psychology · doi:10.1111/j.1464-0597.1992.tb00712.x · Cited by 691 · see citing works
Human Attention in Visual Question Answering: Do Humans and Deep Networks look at the same regions?
Das, A., Agrawal, H., Zitnick, C. L., Parikh, D., & Batra, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1092 · Cited by 431 · see citing works
The enduring impact of transient emotions on decision making
Andrade, E. B., & Ariely, D. (2009) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2009.02.003 · Cited by 362 · see citing works
An Experimental Study on Emotional Reactions Towards a Robot
Pütten, A. M. R.-V. D., Krämer, N. C., Hoffmann, L., Sobieraj, S., & Eimler, S. C. (2012) · International Journal of Social Robotics · doi:10.1007/s12369-012-0173-8 · Cited by 308 · see citing works
Violence detection using Oriented VIolent Flows
Gao, Y., Liu, H., Sun, X., Wang, C., & Liu, Y. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.01.006 · Cited by 284 · see citing works
The role of gender and taste class in the effects of stress on eating.
Grunberg, N. E., & Straub, R. O. (1992) · Health Psychology · doi:10.1037//0278-6133.11.2.97 · Cited by 207 · see citing works
Do emotions help or hurt decision making? a hedgefoxian perspective
Vohs, K. D. (2007) · Cited by 181 · see citing works
Commercial Content Moderation: Digital Laborers' Dirty Work
Roberts, S. (2016) · Scholarship@Western (Western University) · Cited by 160 · see citing works
Extramusical information contributes to emotions induced by music
Vuoskoski, J. K., & Eerola, T. (2013) · Psychology of Music · doi:10.1177/0305735613502373 · Cited by 150 · see citing works
Fast Violence Detection in Video
Déniz, Ó., Serrano, I., Bueno, G., & Kim, T. (2014) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0004695104780485 · Cited by 141 · see citing works
Digital detritus: 'Error' and the logic of opacity in social media content moderation
Roberts, S. (2018) · First Monday · doi:10.5210/fm.v23i3.8283 · Cited by 127 · see citing works
Sutton, T. M., & Altarriba, J. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0598-8 · Cited by 123 · see citing works
Red enhances the processing of facial expressions of anger.
Young, S. G., Elliot, A. J., Feltman, R., & Ambady, N. (2013) · Emotion · doi:10.1037/a0032471 · Cited by 87 · see citing works
Viewer Experience of Obscuring Scene Elements in Photos to Enhance Privacy
Hasan, R., Hassan, E. T., Li, Y., Caine, K. E., Crandall, D., Hoyle, R., & Kapadia, A. C. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173621 · Cited by 78 · see citing works
Governance of and by platforms
Gillespie, T. (2016) · Cited by 75 · see citing works
(2017) · doi:10.1007/978-3-319-32001-4_44-1 · Cited by 43 · see citing works
On Obstructing Obscenity Obfuscation
Rojas–Galeano, S. (2017) · ACM Transactions on the Web · doi:10.1145/3032963 · Cited by 30 · see citing works
But Who Protects the Moderators? The Case of Crowdsourced Image Moderation
Dang, B., Riedl, M. J., & Lease, M. (2018) · arXiv · doi:10.48550/arxiv.1804.10999 · Cited by 26 · see citing works
Jeong, E. J., Biocca, F. A., & Kim, M. K. (2011) · Journal of Korea Game Society · doi:10.7583/jkgs.2011.11.4.127 · Cited by 6 · see citing works
Effect of bright light therapy for depression
Zhuang, Y., Xie, K., & Lin, Y. (2017) · China International Forum on Solid State Lighting & International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) · doi:10.1109/ifws.2017.8245986 · Cited by 5 · see citing works
Diagnostic colours of emotions
Kadar, N. G. (2007) · Europe PMC (PubMed Central) · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 24 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The Psychological Well-Being of Content Moderators
Steiger, M., Bharucha, T. J., Venkatagiri, S., Riedl, M. J., & Lease, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445092 · Cited by 168 · see citing works
A Trade-off-centered Framework of Content Moderation
Jiang, J. A., Nie, P., Brubaker, J. R., & Fiesler, C. L. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3534929 · Cited by 90 · see citing works
Olteanu, A., Garcia-Gathright, J. I., de Rijke, M., Ekstrand, M. D., Roegiest, A., Lipani, A., Beutel, A., Olteanu, A., Lučić, A., Stoica, A.-A., et al. (2019) · ACM SIGIR Forum · doi:10.1145/3458553.3458556 · Cited by 54 · see citing works
Das, A., Dang, B., & Lease, M. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7461 · Cited by 37 · see citing works
Handling and Presenting Harmful Text in NLP Research
Kirk, H. R., Birhane, A., Vidgen, B., & Derczynski, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.35 · Cited by 36 · see citing works
Swain, V. D., Zhong, Q. J., Parekh, J. R., Jeon, Y., Zimmermann, R., Czerwinski, M. P., Suh, J., Mishra, V., Saha, K., & Hernandez, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713705 · Cited by 33 · see citing works
Tseng, E., Ristenpart, T., & Dell, N. L. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711035 · Cited by 11 · see citing works
Son, D., Lew, B., Choi, K., Baek, Y., Choi, S., Shin, B., Ha, S., & Chang, B. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570439 · Cited by 11 · see citing works
AURA: Amplifying Understanding, Resilience, and Awareness for Responsible AI Content Work
Zhang, A. Q., Amores, J., Shen, H., Czerwinski, M. P., Gray, M. L., & Suh, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710931 · Cited by 13 · see citing works
Empowering NGOs in countering online hate messages
Chung, Y.-L., Tekiroğlu, S. S., Tonelli, S., & Guerini, M. (2021) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100150 · Cited by 11 · see citing works
Mitigating Viewer Impact From Disturbing Imagery Using AI Filters: A User-Study
Sarridis, I., Spangenberg, J., Papadopoulou, O., & Papadopoulos, S. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2313890 · Cited by 8 · see citing works
Lee, D., Seo, S., Park, C., Kim, S., Chang, B., & Song, J. Y. (2024) · Conference on Designing Interactive Systems · doi:10.1145/3643834.3660708 · Cited by 10 · see citing works
Investigating Influential Users' Responses to Permanent Suspension on Social Media
Gao, Z., & Thebault-Spieker, J. (2024) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3637356 · Cited by 7 · see citing works
Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models
Sarridis, I., Koutlis, C., Papadopoulou, O., & Papadopoulos, S. (2022) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm55396.2022.00029 · Cited by 6 · see citing works
LeeYouk, S., & Seering, J. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790902 · Cited by 8 · see citing works
Park, S., Kim, J., Choi, J., Seering, J., Lee, U., & Lee, S.-J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757609 · Cited by 4 · see citing works
Tempska, P., Urbaniak, R., Dowgiałło, M., Ptaszynski, M. E., Zajączkowski, A., Milewska, M., Leliwa, G., Marcińczuk, M., Brochocki, M., & Wroczyński, M. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104316 · Cited by 2 · see citing works
Savio, M. T., Balammal, A., de Villa, J. C., Perez, J., Huang, X., & Guevara, R. L. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1666543 · Cited by 1 · see citing works
Research in the information age and the risks of researcher vicarious trauma
Lee, R. (2024) · Social Science Information · doi:10.1177/05390184231223890 · Cited by 1 · see citing works
Elicited emotion: effects of inoculation of an art style on emotionally strong images
Gutierrez, A. C., Tejada, J., & Fernández-Abascal, E. G. (2025) · Experimental Brain Research · doi:10.1007/s00221-025-07030-x · Cited by 3 · see citing works
"Gross!": Personalized Video Content Moderation Using Generative Image Overlays for Aversive Videos
Choi, Y., Kim, D., Jun, Y., Kim, H., & Park, E. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746058.3758458 · Cited by 1 · see citing works
Evaluating image modification as a harm reduction approach in content moderation
Lewitzka, S., Liebeknecht, A., Bridgland, V. M. E., Moeck, E. K., Nixon, R. D. V., Semmler, C., & Takarangi, M. K. T. (2026) · Psychological Research · doi:10.1007/s00426-026-02241-5 · Cited by 1 · see citing works
Kim, S., Jo, Y., Na, S., Lim, H., Lee, E., Choi, Y. M., Cho, S., & Hong, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790820 · Cited by 2 · see citing works
Sanga, S., & Nadendla, V. S. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02708
Can Interpretability Layouts Influence Human Perception of Offensive Sentences?
dos Santos, T. F., Osman, N., & Schorlemmer, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70074-3_3
Gold Standard or Gold-Plated? Human Practices of Triple Verification in CSAM Takedown
Rottier, M., van Eeten, M. J. G., & Zannettou, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791039
Beyond Content Exposure: Systemic Factors Driving Moderators' Mental Health Crisis in Africa
Abdelkadir, N. A., Yang, T., Kapania, S., Malgwi, K. I., Gebrekidan, F. B., Dinika, A.-A. T., Nsoesie, E. O., Miceli, M., & Chancellor, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791639
McDonough, C., Lewitzka, S., Bridgland, V. M. E., Moeck, E. K., Nixon, R. D. V., Semmler, C., & Takarangi, M. K. T. (2026) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2026-4-8
- Authors
- Karunakaran, S., & Ramakrishan, R.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
- Published
- 2019-10-28
- Volume
- 7
- Pages
- 50-58
- ISSN
- 2769-1330 (ISSN-L)
- 2769-1349
- OpenAlex
- W2987974819
- Cited by
- 28
- References
- 24
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 5 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 1 · 2019: 1
- FWCI
- 0.67
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Emotional Labor in Professions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- content moderation, emotional impact, image blurring, user-generated content, review quality, handling time, PANAS
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