Temporal Mental Health Dynamics on Social Media

Tom Tabak, Matthew Purver


Abstract
We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant- supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depres- sion, supported by the literature. We propose a methodology for providing insight into tem- poral mental health dynamics to be utilised for strategic decision-making.
Anthology ID:
2020.nlpcovid19-2.7
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Month:
December
Year:
2020
Address:
Online
Editors:
Karin Verspoor, Kevin Bretonnel Cohen, Michael Conway, Berry de Bruijn, Mark Dredze, Rada Mihalcea, Byron Wallace
Venue:
NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-2.7
DOI:
10.18653/v1/2020.nlpcovid19-2.7
Bibkey:
Cite (ACL):
Tom Tabak and Matthew Purver. 2020. Temporal Mental Health Dynamics on Social Media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
Cite (Informal):
Temporal Mental Health Dynamics on Social Media (Tabak & Purver, NLP-COVID19 2020)
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PDF:
https://aclanthology.org/2020.nlpcovid19-2.7.pdf
Video:
 https://slideslive.com/38939853