@inproceedings{052c59781a244927b5ad66126dec4fc8,
title = "Measuring the latency of depression detection in social media",
abstract = "Detecting depression is a key public health challenge, as almost 12\% of all disabilities can be attributed to depression. Computational models for depression detection must prove not only that can they detect depression, but that they can do it early enough for an intervention to be plausible. However, current evaluations of depression detection are poor at measuring model latency. We identify several issues with the currently popular ERDE metric, and propose a latency-weighted F1 metric that addresses these concerns. We then apply this evaluation to several models from the recent eRisk 2017 shared task on depression detection, and show how our proposed measure can better capture system differences.",
keywords = "Depression, Latency, Neural networks, Social media",
author = "Farig Sadeque and Dongfang Xu and Steven Bethard",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 11th ACM International Conference on Web Search and Data Mining, WSDM 2018 ; Conference date: 05-02-2018 Through 09-02-2018",
year = "2018",
month = feb,
day = "2",
doi = "10.1145/3159652.3159725",
language = "English (US)",
series = "WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",
pages = "495--503",
booktitle = "WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining",
}