id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-320208-uih4jf8w	Li, Diya	Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining	2020-07-10		.txt	text/plain	8951	527	56	In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. In this article, we propose a CorExQ9 algorithm that integrates Correlation Explanation (CorEx) learning algorithm and clinical PHQ lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. We assessed the level of stress expressed in COVID-19 related tweets by integrating a lexicon-based method derived from established clinical assessment questionnaire PHQ-9 [46] . The CorEx algorithm combined with clinical stress measure index (PHQ-9) helped to minimize human interventions and human language ambiguity in social media data mining for stress detection and provided accurate stress symptom measures of Twitter users related to the COVID-19 pandemic.	./cache/cord-320208-uih4jf8w.txt	./txt/cord-320208-uih4jf8w.txt