Danforth, Christopher M.
University of Vermont
7 papers on CoChatORCID 0000-0002-9857-2845
Most cited papers
- Verified
Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011) · PLoS ONE
Psychological Well-being and Life SatisfactionSentiment Analysis and Opinion Mining
Cited by 809
- Verified
Instagram photos reveal predictive markers of depression
Reece, A. G., & Danforth, C. M. (2017) · EPJ Data Science
Mental Health via WritingEmotion and Mood Recognition
Cited by 519
- Verified
Forecasting the onset and course of mental illness with Twitter data
Reece, A. G., Reagan, A. J., Lix, K. L. M., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017) · Scientific Reports
Mental Health via WritingEmotion and Mood Recognition
Cited by 401
- Verified
Human language reveals a universal positivity bias
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., et al. (2015) · Proceedings of the National Academy of Sciences
Sentiment Analysis and Opinion MiningEmotion and Mood Recognition
Cited by 401
- Verified
Doomscrolling during COVID-19: The negative association between daily social and traditional media consumption and mental health symptoms during the COVID-19 pandemic.
Price, M., Legrand, A. C., Brier, Z. M. F., van Stolk-Cooke, K., Peck, K., Dodds, P. S., Danforth, C. M., & Adams, Z. W. (2022) · Psychological Trauma: Theory, Research, Practice, and Policy
COVID-19 and Mental HealthCommunication and COVID-19 Impact
Cited by 141
- Verified
Generalized word shift graphs: a method for visualizing and explaining pairwise comparisons between texts
Gallagher, R. J., Frank, M. R., Mitchell, L., Schwartz, A. J., Reagan, A. J., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science
Computational and Text Analysis MethodsDigital Humanities and Scholarship
Cited by 70
- Verified
Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance
Minot, J. R., Cheney, N., Maier, M., Elbers, D. C., Danforth, C. M., & Dodds, P. S. (2022) · ACM Transactions on Computing for Healthcare
Ethics and Social Impacts of AIMachine Learning in Healthcare
Cited by 23
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