Abstract
The emergence of social media opens tremendous research opportunities. Many individuals, mostly teens and young adults around the world, share their daily lives and opinions about a wide variety of topics (e.g., crime, sports, and politics) on social media sites. Thus, social media becomes a valuable repository for data of different types, which could provide insights of social events happening around the world. However, it is still a challenge to identify the bursty and disruptive events from the massive and noisy user-generated content on social media sites. In this paper, we present a novel event detection framework for identifying surrounding real-world events that can support decision making and emergency management. Our proposed framework consists of four main components, including data pre-processing, event-related tweets classifying, online clustering, and bursty event recognition. We conducted a series of experiments on the real-world social media dataset collected from Twitter. The experimental results demonstrated the effectiveness of our proposed method.
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Ma, W., Liu, Z., Hu, X. (2019). Online Event Detection in Social Media with Bursty Event Recognition. In: Meng, W., Furnell, S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2019. Communications in Computer and Information Science, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-15-0758-8_14
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DOI: https://doi.org/10.1007/978-981-15-0758-8_14
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