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Moral Foundations Twitter Corpus: A Collection of 35k Tweets Annotated for Moral Sentiment
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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.
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“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]
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Xie, J., Hirst, G., & Xu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10762 · Cited by 5
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
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
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Metadata
- 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
- 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 ›
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