Abstract
With the rapid development of Internet, new media, such as blogs, wikis, and social media, become a major platform for information dissemination. Numerous studies focus on event dissemination trend analysis for individual media platform, while very few works are conducted to study the dissemination characteristics in a cross-platform manner. In this work, we propose ESAP, a novel cross-platform approach to analyze the event dissemination trend between social network and search engine simultaneously. ESAP includes three models: an event popularity model based on hot word dynamic weight; a trend similarity model to measure the similarity of event popularity across different platforms over time; and an attention degree model to measure event public attention through time. Experimental results based on four real-world event dissemination datasets (two from Baidu and two from Weibo) produce several interesting findings and validate the effectiveness of ESAP in modeling and analyzing event dissemination trend between social network and search engine from different perspectives.
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Acknowledgments
This work has been supported in part by the China 973 project (2014CB340303), the Opening Project of Baidu (No.181515P005267), it is also supported by the Open Project Program of Shanghai Key Laboratory of Data Science (No.201609060001), the Opening Project of Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) Grant number C15602, and National Natural Science Foundation of China (No. 61472252, 61133006), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20141420 and Grant No. BK20140857).
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Tang, Y., Ma, P., Kong, B., Ji, W., Gao, X., Peng, X. (2016). ESAP: A Novel Approach for Cross-Platform Event Dissemination Trend Analysis Between Social Network and Search Engine. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_36
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