Moral foundations twitter corpus: a collection of 35k tweets annotated for moral sentiment
Open accessAbstract
Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availability of annotated data. To address this issue, we introduce the Moral Foundations Twitter Corpus, a collection of 35,108 tweets that have been curated from seven distinct domains of discourse and hand annotated by at least three trained annotators for 10 categories of moral sentiment. To facilitate investigations of annotator response dynamics, we also provide psychological and demographic metadata for each annotator. Finally, we report moral sentiment classification baselines for this corpus using a range of popular methodologies.
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?
On the MFTC dataset, BERT, despite being applied cross-context, outperformed GPT-4 on the Authority and Purity foundations, and GPT-4-Turbo also on the Loyalty foundation.
“Further comparisons (Tables S7 and S8 in Rathje et al. (75)) on another moral foundations dataset, on which the BERT model was not fine-tuned (MFTC; (81)), showed that BERT, despite being applied cross-context, outperformed GPT-4 on the Authority and Purity foundations, and GPT-4-Turbo also on the Loyalty foundation.”
From Perils and opportunities in using large language models in psychological research (2024) · cites this paper as [81]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106 · see citing works
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Support Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,654 · see citing works
Liberals and conservatives rely on different sets of moral foundations.
Graham, J., Haidt, J., & Nosek, B. A. (2009) · Journal of Personality and Social Psychology · doi:10.1037/a0015141 · Cited by 4,560 · see citing works
The righteous mind: why good people are divided by politics and religion
(2012) · Choice Reviews Online · doi:10.5860/choice.49-7185 · Cited by 4,347 · see citing works
The kappa statistic in reliability studies: use, interpretation, and sample size requirements
Sim, J., & Wright, C. C. (2005) · Physical Therapy · doi:10.1093/ptj/85.3.257 · Cited by 4,093 · see citing works
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610 · see citing works
An Overview of Multi-Task Learning in Deep Neural NetworksPreprint
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 2,409 · see citing works
Graham, J., Haidt, J., Koleva, S. P., Motyl, M. S., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013) · Advances in experimental social psychology · doi:10.1016/b978-0-12-407236-7.00002-4 · Cited by 1,815 · see citing works
Culture as Consensus: A Theory of Culture and Informant Accuracy
Romney, A. K., Weller, S. C., & Batchelder, W. H. (1986) · American Anthropologist · doi:10.1525/aa.1986.88.2.02a00020 · Cited by 1,667 · 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
“…Ji, & Dehghani, 2018), charitable donation (Hoover, Johnson, Boghrati, Graham, & Dehghani, 2018), social avoidance (Dehghani et al., 2016), diffusion (Brady, Wills, Jost, Tucker, & Van Bavel, 2017), and political discourse (Dehghani, Sagae, Sachdeva, & Gratch, 2014; Johnson & Goldwasser, 2018).”
Moral tribes: emotion, reason, and the gap between us and them
(2014) · Choice Reviews Online · doi:10.5860/choice.51-4949 · Cited by 978 · see citing works
Assessing miserly information processing: An expansion of the Cognitive Reflection Test
Toplak, M. E., West, R. F., & Stanovich, K. E. (2014) · Thinking & Reasoning · doi:10.1080/13546783.2013.844729 · Cited by 864 · see citing works
Automatic personality assessment through social media language.
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000020 · Cited by 802 · see citing works
Facebook language predicts depression in medical records
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802331115 · Cited by 643 · see citing works
Multi-task Sequence to Sequence LearningPreprint
Luong, M.-T., Le, Q. V., Sutskever, I., Vinyals, O., & Kaiser, Ł. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06114 · Cited by 606 · see citing works
Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis
Azucar, D., Marengo, D., & Settanni, M. (2017) · Personality and Individual Differences · doi:10.1016/j.paid.2017.12.018 · Cited by 543 · see citing works
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
Yarowsky, D., Baldwin, T., Korhonen, A., Livescu, K., & Bethard, S. J. (2013) · Empirical Methods in Natural Language Processing · Cited by 422 · see citing works
Ellemers, N., van der Toorn, J., Paunov, Y., & van Leeuwen, T. N. (2019) · Personality and Social Psychology Review · doi:10.1177/1088868318811759 · Cited by 414 · see citing works
Gray, K. J., & Graham, J. (2018) · Cited by 391 · see citing works
Kinship, Cooperation, and the Evolution of Moral Systems*
Enke, B. (2019) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz001 · Cited by 343 · see citing works
A solution to the mysteries of morality.
DeScioli, P., & Kurzban, R. (2012) · Psychological Bulletin · doi:10.1037/a0029065 · Cited by 291 · see citing works
Curry, O. S., Jones Chesters, M., & Van Lissa, C. J. (2019) · Journal of Research in Personality · doi:10.1016/j.jrp.2018.10.008 · Cited by 235 · see citing works
Moralization in social networks and the emergence of violence during protests
Mooijman, M., Hoover, J., Lin, Y., Ji, H., & Dehghani, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0353-0 · Cited by 229 · see citing works
Self-citationGaining insights from social media language: Methodologies and challenges.
Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016) · Psychological Methods · doi:10.1037/met0000091 · Cited by 221 · see citing works
Garten, J., Hoover, J., JOHNSON, K., Boghrati, R., Iskiwitch, C., & Dehghani, M. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0875-9 · Cited by 181 · see citing works
Self-citationPurity homophily in social networks.
Dehghani, M., JOHNSON, K., Hoover, J., Sagi, E., Garten, J., Parmar, N., Vaisey, S., Iliev, R. I., & Graham, J. (2016) · Journal of Experimental Psychology General · doi:10.1037/xge0000139 · Cited by 163 · see citing works
Self-citationGarcia, D., & Sikström, S. (2014) · Personality and Individual Differences · doi:10.1016/j.paid.2013.10.001 · Cited by 161 · see citing works
The Benefits of a Model of Annotation
Passonneau, R. J., & Carpenter, B. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00185 · Cited by 151 · cited 1× · see citing works
“…approach because it is a well-known and straightforward method for aggregating human annotations; however, we also believe that applying more sensitive annotation aggregation methods (e.g., see Passonneau & Carpenter, 2014; Paun et al., 2018) to the MFTC will be a fruitful area for future research.”
MethodMeasuring Moral Rhetoric in Text
Sagi, E., & Dehghani, M. (2013) · Social Science Computer Review · doi:10.1177/0894439313506837 · Cited by 121 · see citing works
Self-citationAutomated text analysis in psychology: methods, applications, and future developments
Iliev, R., Dehghani, M., & Sagi, E. (2014) · Language and Cognition · doi:10.1017/langcog.2014.30 · Cited by 119 · see citing works
Self-citationAssessing Rater Performance without a "Gold Standard" Using Consensus Theory
Weller, S. C., & Mann, N. C. (1997) · Medical Decision Making · doi:10.1177/0272989x9701700108 · Cited by 110 · see citing works
Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
Resnik, P., Garron, A., & Resnik, R. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1133 · Cited by 90 · see citing works
Comparing Bayesian Models of Annotation
Paun, S., Carpenter, B., Chamberlain, J., Hovy, D., Kruschwitz, U., & Poesio, M. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00040 · Cited by 86 · see citing works
Classification of Moral Foundations in Microblog Political Discourse
Johnson, K., & Goldwasser, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1067 · Cited by 72 · see citing works
Hoover, J., JOHNSON, K., Boghrati, R., Graham, J., & Dehghani, M. (2018) · Collabra Psychology · doi:10.1525/collabra.129 · Cited by 67 · see citing works
Self-citationIdentifying Patients with Depression Using Free-text Clinical Documents
Zhou, L., Baughman, A. W., Lei, V. J., Lai, K. H., Navathe, A. S., CHANG, F. Y., Margarita, S., Topaz, M., Zhong, F., Murrali, M., et al. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-629 · Cited by 54 · see citing works
Dehghani, M., Sagae, K., Sachdeva, S. S., & Gratch, J. S. (2013) · Journal of Information Technology & Politics · doi:10.1080/19331681.2013.826613 · Cited by 49 · see citing works
Self-citationAcquiring Background Knowledge to Improve Moral Value Prediction
Lin, Y., Hoover, J., Portillo-Wightman, G., Park, C., Dehghani, M., & Ji, H. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508244 · Cited by 46 · see citing works
Self-citationIndividual Differences in the Moralization of Everyday Life
Lovett, B. J., Jordan, A., & Wiltermuth, S. S. (2012) · Ethics & Behavior · doi:10.1080/10508422.2012.659132 · Cited by 36 · see citing works
Moral Foundations Dictionary 2.0Preprint
darloap, C., & Frimer, J. (2019) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/ezn37 · Cited by 20 · see citing works
Measuring the importance of context when modeling language comprehension
Garten, J., Kennedy, B., Sagae, K., & Dehghani, M. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01200-w · Cited by 18 · see citing works
Self-citationIncorporating Demographic Embeddings Into Language Understanding
Garten, J., Kennedy, B., Hoover, J., Sagae, K., & Dehghani, M. (2019) · Cognitive Science · doi:10.1111/cogs.12701 · Cited by 14 · see citing works
Self-citationIndirect reciprocity and the evolution of “moral signals”
Smead, R. (2009) · Biology & Philosophy · doi:10.1007/s10539-009-9175-9 · Cited by 13 · see citing works
Keen, I. (2015) · The Australian Journal of Anthropology · doi:10.1111/taja.12154 · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 50 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Perils and opportunities in using large language models in psychological research
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae245 · Cited by 111 · see citing works
“Further comparisons (Tables S7 and S8in Rathje et al. (75)) on another moral foundations dataset, on which the BERT model was not fine-tuned (MFTC; (81)), showed that BERT, despite being applied cross-context, outperformed GPT-4 on the Authority and Purity foundations, and GPT-4-Turbo also on the…”
Self-citationMacanovic, A. (2022) · Social Science Research · doi:10.1016/j.ssresearch.2022.102784 · Cited by 99 · see citing works
Language-based game theory in the age of artificial intelligence
Capraro, V., Paolo, R. D., Perc, M., & Pizziol, V. (2024) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0720 · Cited by 92 · see citing works
Kennedy, B., Atari, M., Davani, A. M., Yeh, L., Omrani, A., Kim, Y., Coombs, K., Havaldar, S., Portillo-Wightman, G., Gonzalez, E., et al. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-021-09569-x · Cited by 90 · see citing works
Self-citationAgainst Empathy Bias: The Moral Value of Equitable Empathy
Fowler, Z., Law, K. F., & Gaesser, B. (2021) · Psychological Science · doi:10.1177/0956797620979965 · Cited by 91 · see citing works
MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction
Araque, Ó., Gatti, L., & Kalimeri, K. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105184 · Cited by 73 · see citing works
Morality Beyond the WEIRD: How the Nomological Network of Morality Varies Across CulturesPreprint
Atari, M., Haidt, J., Graham, J., Koleva, S. P., Stevens, S. T., & Dehghani, M. (2022) · doi:10.31234/osf.io/q6c9r · Cited by 68 · see citing works
Self-citationText Analysis for Psychology: Methods, Principles, and PracticesPreprint
Kennedy, B., Ashokkumar, A., Boyd, R. L., & Dehghani, M. (2021) · doi:10.31234/osf.io/h2b8t · Cited by 57 · see citing works
Self-citationMoral concerns are differentially observable in language
Kennedy, B., Atari, M., Davani, A. M., Hoover, J., Omrani, A., Graham, J., & Dehghani, M. (2021) · Cognition · doi:10.1016/j.cognition.2021.104696 · Cited by 57 · see citing works
Self-citationProbing Pre-Trained Language Models for Cross-Cultural Differences in Values
Arora, A., Kaffee, L.-A., & Augenstein, I. (2023) · Workshop on Cross-Cultural Considerations in NLP (C3NLP) · doi:10.18653/v1/2023.c3nlp-1.12 · Cited by 91 · see citing works
Reconsidering evidence of moral contagion in online social networks
Burton, J. W., Cruz, N., & Hahn, U. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01133-5 · Cited by 42 · see citing works
Moral Framing and Ideological Bias of News
Mokhberian, N., Abeliuk, A., Cummings, P., & Lerman, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60975-7_16 · Cited by 41 · see citing works
How Imagination and Memory Shape the Moral Mind
O’Connor, B. B., & Fowler, Z. (2022) · Personality and Social Psychology Review · doi:10.1177/10888683221114215 · Cited by 39 · see citing works
Song, Y.-Y., Huang, Z., Schuldt, J. P., & Yuan, Y. (2021) · Journalism · doi:10.1177/1464884921989124 · Cited by 30 · see citing works
How developments in natural language processing help us in understanding human behaviour
Mihalcea, R. F., Biester, L., Boyd, R. L., Jin, Z., Pérez‐Rosas, V., Wilson, S. R., & Pennebaker, J. W. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01938-0 · Cited by 34 · see citing works
Morality classification in natural language text
Pavan, M. C., dos Santos, V. G., Lan, A. G. J., Martins, J. T., dos Santos, W. R., Deutsch, C., da Costa, P. B., Hsieh, F. C., & Paraboni, I. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3034050 · Cited by 30 · see citing works
Mittal, S., & Choudhury, M. D. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580834 · Cited by 34 · see citing works
Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes
Lourie, N., Le Bras, R., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17589 · Cited by 73 · see citing works
Morally Homogeneous Networks and Radicalism
Atari, M., Davani, A. M., Kogon, D., Kennedy, B., Ani Saxena, N., Anderson, I., & Dehghani, M. (2022) · Social Psychological and Personality Science · doi:10.1177/19485506211059329 · Cited by 36 · see citing works
Self-citationCross-Domain Classification of Moral Values
Liscio, E., Dondera, A., Geadau, A., Jonker, C. M., & Murukannaiah, P. K. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.209 · Cited by 26 · see citing works
Lucas, D. S., & Park, U. D. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106271 · Cited by 25 · see citing works
Morality in Our Mind and Across Cultures and Politics
Gray, K. J., & Pratt, S. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-020924-124236 · Cited by 28 · see citing works
Identifying Morality Frames in Political Tweets using Relational Learning
Roy, S., Pacheco, M. L., & Goldwasser, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.783 · Cited by 24 · see citing works
The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
Alshomary, M., Baff, R. E., Gurcke, T., & Wachsmuth, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.601 · Cited by 24 · see citing works
Morality in social media: A scoping review
Neumann, D., & Rhodes, N. D. (2023) · New Media & Society · doi:10.1177/14614448231166056 · Cited by 34 · see citing works
Roy, S., & Goldwasser, D. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.1 · Cited by 23 · see citing works
Kennedy, B., Golazizian, P., Trager, J. P., Atari, M., Hoover, J., Davani, A. M., & Dehghani, M. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad210 · Cited by 24 · see citing works
Self-citationWhat values should an agent align with?
Liscio, E., van der Meer, M., Siebert, L. C., Jonker, C. M., & Murukannaiah, P. K. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09550-0 · Cited by 22 · see citing works
Moral universals: A machine-reading analysis of 256 societies
Alfano, M., Cheong, M., & Curry, O. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25940 · Cited by 22 · see citing works
National politics ignites more talk of morality and power than local politics
Dillion, D., Puryear, C., Li, L., Chiquito, A., & Gray, K. J. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae345 · Cited by 20 · see citing works
Asprino, L., Bulla, L., Giorgis, S. D., Aldo Gangemi, A., Marinucci, L., & Mongiovı̀, M. (2022) · Workshop on Knowledge Extraction and Integration for Deep Learning Architectures: Deep Learning Inside Out (DeeLIO) · doi:10.18653/v1/2022.deelio-1.4 · Cited by 20 · see citing works
Atari, M., Omrani, A., & Dehghani, M. (2023) · doi:10.31234/osf.io/m93pd · Cited by 21 · see citing works
Self-citationA Holistic Framework for Analyzing the COVID-19 Vaccine Debate
Pacheco, M. L., Islam, T., Mahajan, M., Shor, A., Yin, M., Ungar, L., & Goldwasser, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.427 · Cited by 21 · see citing works
Dehumanization and mass violence: A study of mental state language in Nazi propaganda (1927–1945)
Landry, A., Orr, R. I., & Mere, K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274957 · Cited by 19 · see citing works
Hackenburg, K., Brady, W. J., & Manos Tsakiris, A. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad189 · Cited by 19 · see citing works
Rezapour, R., Dinh, L., & Diesner, J. (2021) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3465336.3475112 · Cited by 19 · see citing works
The paucity of morality in everyday talk
Atari, M., Mehl, M. R., Graham, J., Doris, J. M., Schwarz, N., Davani, A. M., Omrani, A., Kennedy, B., Gonzalez, E., Jafarzadeh, N., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-32711-4 · Cited by 17 · see citing works
Self-citationThe science behind “values”: Applying moral foundations theory to strategic foresight
Mills, B., & Wilner, A. S. (2022) · FUTURES & FORESIGHT SCIENCE · doi:10.1002/ffo2.145 · Cited by 13 · see citing works
Reflections on extracting moral foundations from media content
Hopp, F. R., & Weber, R. (2021) · Communication monographs · doi:10.1080/03637751.2021.1963513 · Cited by 17 · see citing works
A Data Fusion Framework for Multi-Domain Morality Learning
Guo, S., Mokhberian, N., & Lerman, K. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22145 · Cited by 17 · see citing works
The moral embeddedness of cryptomarkets: text mining feedback on economic exchanges on the dark web
Macanovic, A., & Przepiorka, W. (2023) · Socio-Economic Review · doi:10.1093/ser/mwad069 · Cited by 17 · see citing works
Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates
Davani, A. M., Díaz, M., Baker, D., & Prabhakaran, V. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659021 · Cited by 16 · see citing works
Self-citationMoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions
Preniqi, V., Ghinassi, I., Ive, J., Saitis, C., & Kalimeri, K. (2024) · Conference on Information Technology for Social Good · doi:10.1145/3677525.3678694 · Cited by 34 · see citing works
Macanovic, A., & Przepiorka, W. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02381-9 · Cited by 17 · see citing works
A survey on moral foundation theory and pre-trained language models: current advances and challenges
Zangari, L., Greco, C. M., Picca, D., & Tagarelli, A. (2025) · AI & Society · doi:10.1007/s00146-025-02225-w · Cited by 16 · see citing works
Exploring morality in argumentation
Kobbe, J., Rehbein, I., Hulpuş, I., & Stuckenschmidt, H. (2020) · MADOC (University of Mannheim) · Cited by 15 · see citing works
Humor meets morality: Joke generation based on moral judgement
Yamane, H., Mori, Y., & Harada, T. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102520 · Cited by 14 · see citing works
Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
Fraser, K., Kiritchenko, S., & Balkır, E. (2022) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2022.trustnlp-1.3 · Cited by 14 · see citing works
van Vliet, L. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.699653 · Cited by 8 · see citing works
Kirk, H. R., Bean, A., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.148 · Cited by 26 · see citing works
Liscio, E., Araque, Ó., Gatti, L., Constantinescu, I., Jonker, C. M., Kalimeri, K., & Murukannaiah, P. K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.789 · Cited by 13 · see citing works
Estimating Value Preferences in a Hybrid Participatory System
Siebert, L. C., Liscio, E., Murukannaiah, P. K., Kaptein, L., Spruit, S. L., van den Hoven, J., & Jonker, C. M. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220193 · Cited by 14 · see citing works
The Blame Game: Understanding Blame Assignment in Social Media
Xi, R., & Singh, M. P. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3261242 · Cited by 12 · see citing works
Eisele, O., Brugman, B. C., & Marschlich, S. (2024) · Public Relations Review · doi:10.1016/j.pubrev.2024.102453 · Cited by 12 · see citing works
Qeirat Values and Victim Blaming in Iran: The Mediating Effect of Culture-Specific Gender Roles
Karimi-Malekabadi, F., & Baboli, M. F. (2022) · Journal of Interpersonal Violence · doi:10.1177/08862605221101184 · Cited by 19 · see citing works
Unraveling moral and emotional discourses on social media: a study of three cases
Chen, Y., Chen, A. X., Yu, H., & Sun, S. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2246551 · Cited by 11 · see citing works
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Burghardt, K., Rao, A., Chochlakis, G., Baruah, S., Guo, S., He, Z., Rojecki, A., Narayanan, S. S., & Lerman, K. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31305 · Cited by 11 · see citing works
Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
Huang, X., Wormley, A. S., & Cohen, A. B. (2022) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3511095.3531269 · Cited by 11 · see citing works
The moral foundations of cryptocurrency: evidence from Twitter and survey research
Banker, S., Park, J., & Chan, E. Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1128575 · Cited by 10 · see citing works
A Corpus for Understanding and Generating Moral Stories
Guan, J., Liu, Z., & Huang, M. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.374 · Cited by 12 · see citing works
Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study
Rao, A., Sabri, N., Guo, S., Raschid, L., & Lerman, K. (2024) · Journal of Medical Internet Research · doi:10.2196/63910 · Cited by 10 · see citing works
Large Language Models meet moral values: A comprehensive assessment of moral abilities
Bulla, L., Giorgis, S. D., Mongiovı̀, M., & Aldo Gangemi, A. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100609 · Cited by 14 · see citing works
Studying Moral-based Differences in the Framing of Political Tweets
Reiter-Haas, M., Kopeinik, S., & Lex, E. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18135 · Cited by 10 · see citing works
A large-scale investigation of everyday moral dilemmas
Yudkin, D. A., Goodwin, G. P., Reece, A. G., Gray, K. J., & Bhatia, S. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf119 · Cited by 9 · see citing works
The humanizing effect of market interaction
Harris, C. M., Myers, A., & Kaiser, A. (2022) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.11.028 · Cited by 9 · see citing works
Yang, E. F., & Yang, S. (2023) · Journal of Health Communication · doi:10.1080/10810730.2023.2217104 · Cited by 9 · see citing works
Exploring Moral Principles Exhibited in OSS: A Case Study on GitHub Heated Issues
Ehsani, R., Rezapour, R., & Chatterjee, P. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613077 · Cited by 9 · see citing works
Do moral values change with the seasons?
Hohm, I., O’Shea, B. A., & Schaller, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313428121 · Cited by 9 · see citing works
Social-LLM: Modeling User Behavior at Scale Using Language Models and Social Network Data
Jiang, J., & Ferrara, E. (2025) · Sci · doi:10.3390/sci7040138 · Cited by 21 · see citing works
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · doi:10.31219/osf.io/tg79n · Cited by 8 · see citing works
Self-citationMorally Homogeneous Networks and RadicalismPreprint
Atari, M., Davani, A. M., Kogon, D., Kennedy, B., Saxena, N. A., Anderson, I. A., & Dehghani, M. (2021) · doi:10.31234/osf.io/h3udp · Cited by 8 · see citing works
Self-citationPerils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · doi:10.31234/osf.io/d695y · Cited by 8 · see citing works
Self-citationXu, M., & Guo, Z. (2022) · Journal of Media Ethics · doi:10.1080/23736992.2022.2159409 · Cited by 7 · see citing works
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research
Vida, K., Simon, J., & Lauscher, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.368 · Cited by 9 · see citing works
Stream: social data and knowledge collective intelligence platform for TRaining Ethical AI Models
Wang, Y., Lu, E., Ruan, Z., Yao, L., & Zeng, Y. (2024) · AI & Society · doi:10.1007/s00146-023-01851-6 · Cited by 7 · see citing works
An unsupervised framework for tracing textual sources of moral change
Ramezani, A., Zhu, Z., Rudzicz, F., & Xu, Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.105 · Cited by 9 · see citing works
LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty.
Araque, Ó., Gatti, L., & Kalimeri, K. (2022) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3524458.3547264 · Cited by 7 · see citing works
Overuse of Moral Language Dampens Content Engagement on Social MediaPreprint
Candia, C., Atari, M., Kteily, N., & Uzzi, B. (2022) · doi:10.31235/osf.io/bzs5e · Cited by 7 · see citing works
Self-citationA Large-Scale Investigation of Everyday Moral DilemmasPreprint
Yudkin, D. A., Goodwin, G. P., Reece, A. G., Gray, K. J., & Bhatia, S. (2023) · doi:10.31234/osf.io/5pcew · Cited by 7 · see citing works
Developing a sentence level fairness metric using word embeddings
Izzidien, A., Fitz, S., Romero, P., Loe, B. S., & Stillwell, D. J. (2022) · International Journal of Digital Humanities · doi:10.1007/s42803-022-00049-4 · Cited by 7 · see citing works
Chao, F., Wang, X., & Yu, G. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.783415 · Cited by 6 · see citing works
The Expression of Moral Values in the Twitter Debate: a Corpus of Conversations
Stranisci, M. A., Leonardis, M. D., Bosco, C., & Patti, V. (2021) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.880 · Cited by 6 · see citing works
Language-based game theory in the age of artificial intelligencePreprint
Capraro, V., Paolo, R. D., Perc, M., & Pizziol, V. (2024) · doi:10.31234/osf.io/cuf8z · Cited by 6 · see citing works
Lindquist, E. G., Abdurahman, S., Woodward, D. W., & West, A. E. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107786 · Cited by 5 · see citing works
Do Differences in Values Influence Disagreements in Online Discussions?
van der Meer, M., Vossen, P. T. J. M., Jonker, C. M., & Murukannaiah, P. K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.992 · Cited by 5 · see citing works
Tracking moral divergence with DDR in presidential debates over 60 years
Xu, M., Hu, L., & Cameron, G. T. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00198-8 · Cited by 5 · see citing works
Detection of Morality in Tweets Based on the Moral Foundation Theory
Bulla, L., Giorgis, S. D., Aldo Gangemi, A., Marinucci, L., & Mongiovı̀, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25599-1_1 · Cited by 5 · see citing works
NewsSlant: Analyzing Political News and Its Influence Through a Moral Lens
Haque, A., & Singh, M. P. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3341910 · Cited by 5 · see citing works
Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussions
Rao, A., Guo, S., Wang, S. Y. N., Morstatter, F., & Lerman, K. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386084 · Cited by 8 · see citing works
Malik, M., Youk, S., Hopp, F. R., Curry, O. S., Cheong, M., Alfano, M., & Weber, R. (2025) · Communication Methods and Measures · doi:10.1080/19312458.2025.2500329 · Cited by 6 · see citing works
A tale of two hashtags: An examination of moral content of pro‐ and anti‐government tweets in Turkey
Bayrak, F., & Alper, S. (2021) · European Journal of Social Psychology · doi:10.1002/ejsp.2763 · Cited by 3 · see citing works
Text- and author-dependent moral foundations classification
Lan, A. G. J., & Paraboni, I. (2022) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2022.2092655 · Cited by 5 · see citing works
Contextualized moral inferencePreprint
Xie, J., Hirst, G., & Xu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10762 · Cited by 5 · see citing works
NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social MediaPreprint
Garg, M., Saxena, C., Naseem, U., Sohn, S., & Dorr, B. J. (2023) · doi:10.36227/techrxiv.21972974.v1 · Cited by 5 · see citing works
Moral Foundation Measurements Fail to Converge on Multilingual Party Manifestos
Stecker, M., & Hopp, F. R. (2025) · Political Analysis · doi:10.1017/pan.2025.10011 · Cited by 4 · see citing works
Qiu, Y., Ye, Y., Zhang, X., & Luo, J. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825487 · Cited by 4 · see citing works
- Authors
- Hoover, J., Portillo-Wightman, G., Yeh, L., Havaldar, S., Davani, A. M., Lin, Y., Kennedy, B., Atari, M., Kamel, Z., Mendlen, M., Moreno, G., Park, C., Chang, T. E., Chin, J., Leong, C., Leung, J. Y., Mirinjian, A., & Dehghani, M.
- Affiliations
- University of Southern California (US)
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Dehghani, Morteza
- ORCID
- Venue
- Social Psychological and Personality Science
- Publisher
- SAGE Publications
- Published
- 2020-02-19
- Volume
- 11
- Issue
- 8
- Pages
- 1057-1071
- ISSN
- 1948-5506 (ISSN-L)
- 1948-5514
- OpenAlex
- W3008655042
- Semantic Scholar
- a95ee39bda53cf7cbf1677d1bbac9d2be34fcc5a
- Cited by
- 177
- References
- 50
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 36 · 2023: 41 · 2022: 39 · 2021: 24 · 2020: 7 · 2019: 1
- FWCI
- 10.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Social Psychological and Personality Science (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- Army Research Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Keywords
- moral sentiments, moral foundations, Twitter, annotated corpus, social media discourse, message dissemination, protest dynamics, social distancing
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
