The dynamics of health behavior sentiments on a large online social network
Open accessAbstract
Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent.
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The automation of sentiment detection on social networks has gained attention for various purposes.
“The automation of sentiment detection on these social networks has gained attention for various purposes [18][19][20].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [20]
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- Authors
- Salathé, M., Vu, D. Q., Khandelwal, S., & Hunter, D. R.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Salathé, Marcel
- Venue
- EPJ Data Science
- Published
- 2013-04-04
- Volume
- 2
- Issue
- 1
- ISSN
- 2193-1127 (ISSN-L)
- OpenAlex
- W2169734855
- Cited by
- 116
- References
- 47
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 9 · 2021: 11 · 2020: 16 · 2019: 17 · 2018: 8 · 2017: 10 · 2016: 11 · 2015: 12 · 2014: 8 · 2013: 2
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- EPJ Data Science (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Science Foundation (N00014-08-1-1015, 0821527)
- National Institutes of Health
- Office of Naval Research (N000140811015, N00014)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Keywords
- health behavior, sentiment dynamics, social contagion, peer influence, homophily, neighborhood size, exposure intensity, vaccine attitudes, Twitter, social networking sites
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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