Time-critical geolocation for social good
المؤلف | Suwaileh, Reem |
تاريخ الإتاحة | 2024-03-11T06:03:06Z |
تاريخ النشر | 2020 |
اسم المنشور | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
المصدر | Scopus |
الرقم المعياري الدولي للكتاب | 3029743 |
الملخص | Twitter 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. |
راعي المشروع | Acknowledgments. 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. |
اللغة | en |
الناشر | Springer |
الموضوع | Geolocation Social good |
النوع | Conference Paper |
الصفحات | 624-629 |
رقم المجلد | 12036 LNCS |
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