Sensing Real-World Events Using Arabic Twitter Posts
Nasser Alsaedi, Pete Burnap, Omer Rana
Abstract
In recent years, there has been increased interest in eventdetection using data posted to social media sites. Automaticallytransforming user-generated content into informationrelating to events is a challenging task due to the short informallanguage used within the content and the variety oftopics discussed on social media. Recent advances in detectingreal-world events in English and other languages havebeen published. However, the detection of events in the Arabiclanguage has been limited to date. To address this task, wepresent an end-to-end event detection framework which comprisessix main components: data collection, pre-processing,classification, feature selection, topic clustering and summarization.Large-scale experiments over millions of ArabicTwitter messages show the effectiveness of our approach fordetecting real-world event content from Twitter posts.