id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-285721-2fimkpd8	Kejriwal, Mayank	On detecting urgency in short crisis messages using minimal supervision and transfer learning	2020-07-08		.txt	text/plain	6323	307	54	To the best of our knowledge, this is the first such paper investigating the problem of urgency detection in social media, both algorithmically and empirically, for arbitrary disasters in low-supervision and transfer learning settings. More generally, projects like CrisisLex, Crisis Computing 4 and EPIC (Empowering the Public with Information in Crisis) have emerged as major efforts in the crisis informatics space due to two reasons: First, the abundance and fine granularity of social media data implies that mining such data during crises can lead to robust, real-time responses; second, the recognition that any technology that is thus developed must also address the inherent challenges (including problems of noise, scale and irrelevance) in working with such datasets. Other relevant work in crisis informatics, both in terms of defining 'actionable information' problems like urgency and need mining, and providing multimodal Twitter datasets from natural disasters, may be found in (He et al.	./cache/cord-285721-2fimkpd8.txt	./txt/cord-285721-2fimkpd8.txt