In recent years, there has been increased interest in event detection using data posted to social media sites. Automatically transforming user-generated content into information relating to events is a challenging task due to the short informal language used within the content and the variety of topics discussed on social media. Recent advances in detecting real-world events in English and other languages have been published. However, the detection of events in the Arabic language has been limited to date. To address this task, we present an end-to-end event detection framework which comprises six main components: data collection, pre-processing, classification, feature selection, topic clustering and summarization. Large-scale experiments over millions of Arabic Twitter messages show the effectiveness of our approach for detecting real-world event content from Twitter posts.
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