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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.nlpcovid19-2.7.pdf