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Twitter estimates of county-level mental health during COVID-19

Mental health metrics, namely psychological stress, lonely expressions, anxiety, and sentiment are measured daily using pre-trained machine learning models applied to a random 1% Twitter data. For more details, read our publication in the Journal of General Internal Medicine: http://wwbp.org/papers/jgim-2020.pdf

Dataset is available (upon request) at: https://zenodo.org/record/4064043

Data snapshot:

group_id feat value group_norm day cnty
2020-04-16:01001 lonely_score 78 2.92268402613488 2020-04-16 01001
2020-04-16:01003 lonely_score 830 2.82928758282208 2020-04-16 01003
2020-04-16:01005 lonely_score 13 3.4083486715075 2020-04-16 01005
2020-04-16:01017 lonely_score 93 2.67820445675611 2020-04-16 01017
2020-04-16:01021 lonely_score 96 3.02387743147066 2020-04-16 01021

cnty: FIPS code of county

day: date

group_norm: mental health estimate; a sum of term relative frequencies weighted by their association with this mental health outcome in the pre-trained model

value: number of words contributing to the estimate

feat: descriptor of metric

group_id: concatenation of day:cnty

This data (aggregated to the state-level) is also used to update the Penn COVID Twitter Map https://penncovid19hub.com/twitter-map

Citation

APA:

Guntuku, S. C., Sherman, G., Stokes, D. C., Agarwal, A. K., Seltzer, E., Merchant, R. M., & Ungar, L. H. (2020). Tracking Mental Health and Symptom Mentions on Twitter During COVID-19. Journal of general internal medicine, 1-3.

Bib:

@article{guntuku2020tracking,
  title={Tracking Mental Health and Symptom Mentions on Twitter During COVID-19},
  author={Guntuku, Sharath Chandra and Sherman, Garrick and Stokes, Daniel C and Agarwal, Anish K and Seltzer, Emily and Merchant, Raina M and Ungar, Lyle H},
  journal={Journal of general internal medicine},
  pages={1--3},
  year={2020},
  publisher={Springer}
}

Details of sentiment, stress, and loneliness models are described in the following papers: Sentiment:

Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436-465.

Stress:

Guntuku, S. C., Buffone, A., Jaidka, K., Eichstaedt, J. C., & Ungar, L. H. (2019, July). Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 214-225).

Loneliness:

Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., ... & Merchant, R. (2019). Studying expressions of loneliness in individuals using twitter: an observational study. BMJ open, 9(11).

Anxiety:

Schwartz HA, Eichstaedt J, Kern M, Park G, Sap M, Stillwell D, Kosinski M, Ungar L. Towards assessing changes in degree of depression through Facebook. In Proceedings of the workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality. Baltimore: Association for Computational Linguistics; 2014. p. 118–125.

Guntuku, S. C., Preotiuc-Pietro, D., Eichstaedt, J. C., & Ungar, L. H. (2019, July). What twitter profile and posted images reveal about depression and anxiety. In Proceedings of the international AAAI conference on web and social media (Vol. 13, pp. 236-246).

For any queries, please reach out at sharathg at cis dot upenn dot edu or garricks at sas dot upenn dot edu.