FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events

Btool Hamoui, Mourad Mars, Khaled Almotairi


Abstract
The rise of social media platforms makes it a valuable information source of recent events and users’ perspective towards them. Twitter has been one of the most important communication platforms in recent years. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. Detecting events from tweets can help to predict real-world events precisely. A serious challenge that faces Arabic event detection is the lack of Arabic datasets that can be exploited in detecting events. This paper will describe FloDusTA, which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. The process of building the dataset starting from tweets collection to annotation by human annotators will be present. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The dataset was tested for classification and the result was strongly encouraging.
Anthology ID:
2020.lrec-1.174
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1391–1396
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.174
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2020.lrec-1.174.pdf