Human language reveals a universal positivity bias
Open accessAbstract
Significance: The most commonly used words of 24 corpora across 10 diverse human languages exhibit a clear positive bias, a big data confirmation of the Pollyanna hypothesis. The study’s findings are based on 5 million individual human scores and pave the way for the development of powerful language-based tools for measuring emotion.
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Visitors' responses to survey questions may reflect an inherently positive assessment as a result of confirmation bias because they made a high investment in their travel.
“Since visitors made a high investment in their travel, their responses to the survey questions may reflect an inherently positive assessment as a result of confirmation bias (Dodds et al., 2015).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Dodds et al., 2015
One reason for better positive classification might be the existence of a larger number of positive texts and lexicons' bias towards positivity, as human language is inherently biased towards positivity.
“One reason might be the existence of a larger number of positive texts and lexicons' bias towards positivity, as human language is inherently biased towards positivity (Dodds et al., 2015).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Dodds et al., 2015
Cites
The Secret Life of Pronouns: Flexibility in Writing Style and Physical Health
Campbell, R. S., & Pennebaker, J. W. (2003) · Psychological Science · doi:10.1111/1467-9280.01419 · Cited by 430 · see citing works
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Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997 · see citing works
Sentiment Analysis in Tourism: Capitalizing on Big Data
Alaei, A. R., Becken, S., & Stantic, B. (2019) · Journal of Travel Research · doi:10.1177/0047287517747753 · Cited by 622 · see citing works
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Reece, A. G., Reagan, A. J., Lix, K. L. M., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12961-9 · Cited by 401 · see citing works
Self-citationVinkers, C. H., Tijdink, J. K., & Otte, W. M. (2015) · BMJ · doi:10.1136/bmj.h6467 · Cited by 237 · see citing works
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Watson, J., van der Linden, S., Watson, M., & Stillwell, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-71263-z · Cited by 70 · see citing works
The dynamics of norm change in the cultural evolution of language
Amato, R., Lacasa, L., Díaz-Guilera, A., & Baronchelli, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721059115 · Cited by 62 · see citing works
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Wu, D., Wu, H., & Li, J. (2024) · Scientometrics · doi:10.1007/s11192-024-05074-4 · Cited by 8 · see citing works
- Authors
- Dodds, P. S., Clark, E. M., Desu, S., Frank, M. R., Reagan, A. J., Williams, J. R., Mitchell, L., Harris, K. D., Kloumann, I. M., Bagrow, J. P., Megerdoomian, K., McMahon, M. T., Tivnan, B. F., & Danforth, C. M.
- Affiliations
- University of Vermont (US)
- Massachusetts Institute of Technology (US)
- The University of Adelaide (AU)
- University of Washington (US)
- Cornell University (US)
- Mitre (United States) (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-02-09
- Volume
- 112
- Issue
- 8
- Pages
- 2389-2394
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4345622
- OpenAlex
- W2022792389
- Cited by
- 401
- References
- 1
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 44 · 2023: 56 · 2022: 42 · 2021: 39 · 2020: 25 · 2019: 25 · 2018: 24 · 2017: 33 · 2016: 31 · 2015: 17 · 2014: 1
- FWCI
- 34.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- ScholarWorks -A service of University of Vermont Libraries (University of Vermont) (submittedVersion, cc-by-nc-nd)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- NSF | SBE | Division of Social and Economic Sciences (0846668)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- positivity bias, human language, cross-linguistic comparison, word frequency, interlanguage variation, human sociality, emotion measurement
- MeSH terms
- Emotions, Humans, Language, Time Factors, Bias
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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.
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