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• W2911005318 abstract "In today's big data era, breaking events are disseminating on various platforms like social media and search engine at a high velocity, generating large volumes of event-related data. Numerous studies focus on the event dissemination trend analysis on a particular media platform, while very few works are conducted to adaptively identify hot words and analyze an event's popularity in a platform independent manner from streaming data. In this work, we propose GAPES(General Analysis and Prediction of Event popularity on Streaming data), a novel and unified approach to analyze and predict the event popularity on different media platforms. GAPES first processes event streaming data and adaptively selects event hot words by considering word's importance, correlation and completeness aspects, then it uses an unified event popularity analysis model and an probability distribution based event popularity prediction model to accurately analyze and forecast event trend with a flexible timescale. Experimental results based on 16 real-world event datasets produce several interesting findings and validate the capability of GAPES in adaptively selecting event's representative words, effectively analyzing and accurately predicting event popularity on both social network and search engine platforms." @default.
• W2911005318 created "2019-01-25" @default.
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• W2911005318 date "2018-12-01" @default.
• W2911005318 modified "2023-09-27" @default.
• W2911005318 title "Adaptive General Event Popularity Analysis on Streaming Data" @default.
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• W2911005318 doi "https://doi.org/10.1109/bdcat.2018.00022" @default.
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