Embedding Societal Values into Social Media Algorithms
Open accessAbstract
In this commentary, we refer to "AI" and "algorithm" interchangeably to refer to machine learning procedures that learn to predict from large-scale data.We are primarily concerned with social media algorithms focused on ranking and recommendation, especially feed algorithms, but we note that social media AIs play many other roles as well, including content moderation, (de)monetization, misinformation tagging, political content tagging, and toxicity judgments.
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The interventions may result in algorithms that reduce partisan animosity and promote greater social trust and healthier democratic discourse across party lines.
“Together, these interventions may result in algorithms that not only reduce partisan animosity but also promote greater social trust and healthier democratic discourse across party lines (38,40).”
From Reranking partisan animosity in algorithmic social media feeds alters affective polarization (2025) · cites this paper as [38]
The section on testing the AI model reports that feeds embedding democratic values reduced partisan animosity in preregistered experiments. Sections 3 and 4 discuss fostering mutual respect and promoting civil discourse as possible algorithmic goals, but the paper does not establish that these interventions increase social trust or improve discourse across party lines. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe real-time reranking infrastructure with LLMs can create a ranking intervention that more directly targets the focal hypothesis without needing platform collaboration.
“However, by connecting our real-time reranking infrastructure with recent advances in LLMs, we can create a ranking intervention that more directly targets the focal hypothesis (38) without needing platform collaboration.”
From Reranking partisan animosity in algorithmic social media feeds alters affective polarization (2025) · cites this paper as [38]
The paper’s section on testing AI models against human annotations describes using GPT-4 to rate posts on variables drawn from a social-science construct and using those ratings to redesign feed ranking. That supports the idea of an LLM-based intervention targeting a hypothesis, but the cited paper does not describe the citing authors’ real-time reranking infrastructure or establish that it works without platform collaboration. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cites
The structural transformation of the public sphere: an inquiry into a category of bourgeois society
Habermas, J., Bürger, T., & Lawrence, F. P. (1990) · Choice Reviews Online · doi:10.5860/choice.27-4175 · Cited by 12,640 · see citing works
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1× · see citing works
“This may explain why politically polarizing content and fake news that contain high arousal negative affect are more likely to spread in the US (Vosoughi, Roy, and Aral 2018).”
Rethinking the Public Sphere: A Contribution to the Critique of Actually Existing Democracy
Fraser, N. (1990) · Social Text · doi:10.2307/466240 · Cited by 5,147 · see citing works
Nature and Operation of Attitudes
Ajzen, I. (2001) · Annual Review of Psychology · doi:10.1146/annurev.psych.52.1.27 · Cited by 4,321 · see citing works
An overview of the Schwartz theory of basic values
Schwartz, S. H. (2012) · Online Readings in Psychology and Culture · doi:10.9707/2307-0919.1116 · Cited by 4,147 · see citing works
Toward a universal psychological structure of human values.
Schwartz, S. H., & Bilsky, W. (1987) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.53.3.550 · Cited by 3,617 · see citing works
Sentiment analysis algorithms and applications: A survey
Medhat, W., Hassan, A., & Korashy, H. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.011 · Cited by 3,265 · see citing works
Emotion shapes the diffusion of moralized content in social networks
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618923114 · Cited by 1,396 · cited 1× · see citing works
“…that contain highly arousing negative content like anger, disgust, and fear (Hsu et al. 2021), and are more likely to share that content with others, perhaps because those states violate their values and therefore hijack their attention (Bellovary, Young, and Goldenberg 2021; Brady et al. 2017).”
Evaluating the structure of human values with confirmatory factor analysis
Schwartz, S. H., & Boehnke, K. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00069-2 · Cited by 1,093 · see citing works
Algorithms as culture: Some tactics for the ethnography of algorithmic systems
Seaver, N. (2017) · Big Data & Society · doi:10.1177/2053951717738104 · Cited by 1,010 · see citing works
Gillespie, T. (2019) · doi:10.12987/9780300235029 · Cited by 806 · see citing works
Values as truisms: Evidence and implications.
Maio, G. R., & Olson, J. M. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.74.2.294 · Cited by 284 · see citing works
Tweets, Tweeps, and Signifyin’: Communication and Cultural Performance on “Black Twitter”
Florini, S. (2014) · Television & New Media · doi:10.1177/1527476413480247 · Cited by 271 · see citing works
Feedback Loop and Bias Amplification in Recommender Systems
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., & Burke, R. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412152 · Cited by 240 · see citing works
Trick and Please. A Mixed-Method Study On User Assumptions About the TikTok Algorithm
Klug, D., Qin, Y., Evans, M. C., & Kaufman, G. F. (2021) · ACM Web Science Conference (WebSci) · doi:10.1145/3447535.3462512 · Cited by 238 · see citing works
Value-Sensitive Algorithm Design: Method, Case Study, and Lessons
Zhu, H., Yu, B., Halfaker, A., & Terveen, L. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274463 · Cited by 220 · see citing works
IF … THEN: Algorithmic power and politics
Nyre, L. (2019) · New Media & Society · doi:10.1177/1461444819832541 · Cited by 199 · see citing works
Sims, T. L., Tsai, J. L., Jiang, D., Wang, Y., Fung, H. H.-L., & Zhang, X. (2015) · Journal of Personality and Social Psychology · doi:10.1037/a0039276 · Cited by 163 · see citing works
Self-citationA New Generation of Perspective API: Efficient Multilingual Character-level Transformers
Lees, A., Tran, V. Q., Tay, Y., Sorensen, J., Gupta, J., Metzler, D., & Vasserman, L. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539147 · Cited by 155 · see citing works
Guy, I., Ronen, I., & Wilcox, E. (2009) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1502650.1502664 · Cited by 154 · see citing works
Estimating peer effects in networks with peer encouragement designs
Eckles, D., Kizilcec, R. F., & Bakshy, E. (2016) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1511201113 · Cited by 141 · see citing works
Angry by design: toxic communication and technical architectures
Munn, L. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00550-7 · Cited by 119 · see citing works
Social Comparison and Facebook
Burke, M., Cheng, J., & de Gant, B. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376482 · Cited by 57 · see citing works
Bellovary, A. K., Young, N. A., & Goldenberg, A. (2021) · Affective Science · doi:10.1007/s42761-021-00046-w · Cited by 54 · see citing works
Hsu, T. W., Niiya, Y., Thelwall, M., Ko, M., Knutson, B., & Tsai, J. L. (2021) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000282 · Cited by 50 · see citing works
Self-citationEmbedding Democratic Values into Social Media AIs via Societal Objective Functions
Jia, C., Lam, M. S., Mai, M. C., Hancock, J. T., & Bernstein, M. S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641002 · Cited by 31 · see citing works
Self-citationUnderstanding Social Media Recommendation Algorithms
Narayanan, A. (2023) · Columbia Academic Commons (Columbia University) · doi:10.7916/khdk-m460 · Cited by 27 · see citing works
Serving a Billion Personalized News Feeds
Bäckström, L. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835848 · Cited by 15 · see citing works
Algorithmic transparency and assessing effects of algorithmic ranking
Eckles, D. (2022) · doi:10.31235/osf.io/c8za6 · Cited by 12 · see citing works
Attitudes, behavior, and institutional inversion: The case of debt.
Cohen, D., Shin, F., & Lawless, R. M. (2021) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000265 · Cited by 9 · see citing works
The Nature of Human Values.Milton Rokeach
Gutek, B. A. (1975) · American Journal of Sociology · doi:10.1086/226092 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Hassan, Naeemul, Bill Adair, James T. Hamilton, Chengkai Li, Mark Tremayne, Jun Yang, and Cong Yu. 2015. "The quest to automate fact-checking." In Proceedings of the 2015 Computation+ Journalism Symposium. October. https://www.researchgate.ne t/publication/301801279_The_Quest_to_Automate_Fact-Checking.
- Skalse, Joar, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger. 2022. "Defining and characterizing reward gaming." Advances in Neural Information Processing Systems 35:9460-71.
Cited by
Sorted by citations
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
Piccardi, T., Saveski, M., Jia, C., Hancock, J., Tsai, J. L., & Bernstein, M. S. (2025) · Science · doi:10.1126/science.adu5584 · Cited by 34 · see citing works
“Together, these interventions may result in algorithms that not only reduce partisan animosity but also promote greater social trust and healthier democratic discourse across party lines ( 38 , 40 ).”
Self-citationEmbedding Democratic Values into Social Media AIs via Societal Objective Functions
Jia, C., Lam, M. S., Mai, M. C., Hancock, J. T., & Bernstein, M. S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641002 · Cited by 31 · see citing works
Self-citationCommunity notes reduce engagement with and diffusion of false information online
Slaughter, I., Peytavin, A., Ugander, J. H., & Saveski, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2503413122 · Cited by 23 · see citing works
Self-citationDecentralized social networks and the future of free speech online
Huang, T. (2024) · Computer Law & Security Review · doi:10.1016/j.clsr.2024.106059 · Cited by 15 · see citing works
Choi, F., Lambert, C., Koshy, V., Pratipati, S., Do, T., & Chandrasekharan, E. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713521 · Cited by 6 · see citing works
KARAKOÇ, E., & Demirel, E. S. (2025) · Akdeniz Üniversitesi İletişim Fakültesi Dergisi · doi:10.31123/akil.1628894 · Cited by 3 · see citing works
Degenerative ‘Affordance’ of Social Media in Family Business
Irene, B., Irene, J., Lockyer, J., & Dewitt, S. (2025) · Systems · doi:10.3390/systems13080629 · Cited by 3 · see citing works
New Worlds Arise: Online Trust and Safety
Tyler, T. R., Meares, T. L., & Katsaros, M. (2024) · Annual Review of Criminology · doi:10.1146/annurev-criminol-111523-122337 · Cited by 4 · see citing works
Solutions and Challenges for Addressing Misinformation
Martel, C., & Rand, D. G. (2025) · Journal of Public Policy & Marketing · doi:10.1177/07439156251352415 · Cited by 3 · see citing works
Decentralized Social Networks and the Future of Free Speech OnlinePreprint
Huang, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4879095 · Cited by 3 · see citing works
Using LLMs to Enhance Democracy
Lazar, S., & Manuali, L. (2026) · Minds and Machines · doi:10.1007/s11023-026-09767-y · Cited by 12 · see citing works
In Moderation: Automation in the Digital Public Sphere
Acosta-Navas, D. (2025) · Journal of Business Ethics · doi:10.1007/s10551-024-05912-8 · Cited by 4 · see citing works
The moral case for using language model agents for recommendation
Lazar, S., Thorburn, L., Tian, J. W., & Belli, L. (2025) · Inquiry · doi:10.1080/0020174x.2025.2515579 · Cited by 2 · see citing works
Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically Polarized Posts on Twitter
Hanley, H. W. A., & Durumeric, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757684 · Cited by 2 · see citing works
Xu, C., & Ge, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757396 · Cited by 1 · see citing works
Self-citationWang, L., Gao, Y., Anyi, C. L., & Goel, A. K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790279 · Cited by 3 · see citing works
Erben, Ş. E. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102843 · Cited by 1 · see citing works
Decentralized Social Networks and the Future of Free Speech OnlinePreprint
Huang, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4863943
Reimagining Human-AI Social Collaboration
Wang, L., & Goel, A. K. (2025) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v5i1.35575
Wen, Y. (2025) · MATEC Web of Conferences · doi:10.1051/matecconf/202541307010
Refreshing social media typology for youths
Tay, A., Jiow, H. J., Affandi, N. S. B., Heng, D., Nasar, A. H. B., & Amran, F. F. B. (2025) · Communication Research and Practice · doi:10.1080/22041451.2025.2588734
Del-Valle-Soto, C., Corona, V., GomezRomero-Borquez, J., Contreras-Tiscareno, D., Montoya-Rodriguez, D. S., Gutierrez-Calvillo, J. A., Sandoval, B., & Aldás, J. V. (2026) · Technologies · doi:10.3390/technologies14010070
Comparing Human and Virtual Influencers: How Self-Disclosure Shapes Engagement on InstagramPreprint
Sew, C., Gunasekara, S., Ma, Y., Teng, S., Genay, A., Kelly, R. M., & Gonçalves, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6172316
KARAKOÇ, E., & Demirel, E. S. (2025) · DergiPark (Istanbul University)
Chen, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6369178
Psychological Agentic AI: Anchoring Authenticity on Algorithmic Needs
Afolabi, F. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798361
Popowski, L., Wu, X., Zhu, X., Piccardi, T., & Bernstein, M. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791569
Self-citationAlgorithms and Targeted Content
Erdoğan, E. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_61-1
Guiding Methodological Choices in Digital Ethnography: A Decision-Making Framework
Basaraba, N. (2026) · Journal of Contemporary Ethnography · doi:10.1177/08912416261476483
Algorithmic systems, human agency and the future of platform research
Hosseinmardi, H., Dutta, U., Rothschild, D. M., & Watts, D. J. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01038-1
The Dissemination Function and Social Role of Film and Television Works
Yun, L. (2026) · SHS Web of Conferences · doi:10.1051/shsconf/202624204006
- Authors
- Bernstein, M., Christin, A., Hancock, J., Hashimoto, T., Jia, C., Lam, M., Meister, N., Persily, N., Piccardi, T., Saveski, M., Tsai, J., Ugander, J., & Xu, C.
- Affiliations
- Stanford University (US)
- Northeastern University (US)
- University of Washington (US)
- ORCID
- Venue
- Journal of Online Trust and Safety
- Publisher
- Stanford Internet Observatory
- Published
- 2023-09-21
- Volume
- 2
- Issue
- 1
- ISSN
- 2770-3142 (ISSN-L)
- OpenAlex
- W4386966581
- Cited by
- 31
- References
- 31
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 5
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Online Trust and Safety (publishedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- social values, social media algorithms, algorithmic ranking, recommendation algorithms, content moderation, machine learning
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