Twitter Event Tracking Based on Segmentation
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Abstract
The domain of text mining has witnessed an increase in interest regarding event detection, especially with the wealth of information accessible on social media sites such as Twitter. Twitter's unique features such as hashtags and character limits enable quick reporting of real-world events, making it an invaluable resource. While previous studies have mainly focused on localized or breaking news events, many significant occurrences have been overlooked. This paper tackles the challenges of event identification using Twitter and presents SEDTWik, a system that leverages tweet segmentation to identify noteworthy events across various locations and categories. The approach involves segmenting tweets and hashtags, detecting bursty segments, clustering them, and summarizing the results. Evaluation on the Events2012 corpus demonstrates the system's outstanding performance. Key terms include Wikipedia, text mining, Twitter, social media, microblogging, tweet segmentation, and event detection.
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