@inproceedings{TabakPurver20EMNLPCOVID,
    title = "Temporal Mental Health Dynamics on Social Media",
    author = "Tabak, Tom  and  Purver, Matthew",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/2020.nlpcovid19-2.7",
    url = "http://www.eecs.qmul.ac.uk/~mpurver/papers/tabak-purver20emnlpcovid.pdf",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.7",
    url = "http://doi.org/10.18653/v1/2020.nlpcovid19-2.7",
    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 Depression, supported by the literature. We propose a methodology for providing insight into temporal mental health dynamics to be utilised for strategic decision-making.",
}