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AuthorSuwaileh, Reem
Available date2024-03-11T06:03:06Z
Publication Date2020
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
ISSN3029743
URIhttp://dx.doi.org/10.1007/978-3-030-45442-5_82
URIhttp://hdl.handle.net/10576/52841
AbstractTwitter has become an instrumental source of news in emergencies where efficient access, dissemination of information, and immediate reactions are critical. Nevertheless, due to several challenges, the current fully-automated processing methods are not yet mature enough for deployment in real scenarios. In this dissertation, I focus on tackling the lack of context problem by studying automatic geo-location techniques. I specifically aim to study the Location Mention Prediction problem in which the system has to extract location mentions in tweets and pin them on the map. To address this problem, I aim to exploit different techniques such as training neural models, enriching the tweet representation, and studying methods to mitigate the lack of labeled data. I anticipate many downstream applications for the Location Mention Prediction problem such as incident detection, real-time action management during emergencies, and fake news and rumor detection among others.
SponsorAcknowledgments. This work was made possible by GSRA grant# GSRA5-1-0527-18082 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer
SubjectGeolocation
Social good
Twitter
TitleTime-critical geolocation for social good
TypeConference Paper
Pagination624-629
Volume Number12036 LNCS
dc.accessType Abstract Only


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