id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-309790-rx9cux8i	Sarker, Abeed	Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource	2020-07-04		.txt	text/plain	2677	152	54	MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. With this in mind, we explored the possibility of using social media, namely Twitter, to study symptoms self-reported by users who tested positive for COVID-19. Our primary goals were to (i) verify that users report their experiences with COVID-19-including their positive test results and symptoms experienced-on Twitter, and (ii) compare the distribution of self-reported symptoms with those reported in studies conducted in clinical settings. Our secondary objectives were to (i) create a COVID-19 symptom corpus that captures the multitude of ways in which users express symptoms so that natural language processing (NLP) systems may be developed for automated symptom detection, and (ii) collect a cohort of COVID-19-positive Twitter users whose longitudinal self-reported information may be studied in the future.	./cache/cord-309790-rx9cux8i.txt	./txt/cord-309790-rx9cux8i.txt