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題名:	Social Event Magnitudes via Background Influences and Engagement Capacities and its Applications
作者:	劉吉軒 Liu, Jyi-shane Liu, Kwei-Guu
貢獻者:	資科系
日期:	2019-06
上傳時間:	2020-04-22 15:39:04 (UTC+8)
摘要:	Outbreaks of social events can be viewed from two angles: anomalous changes of information or popular actions. Event detection algorithms focus on the former one, while the later one is measured by social event intensity, which is a rate to show how popular an action is in a social network at a given time. The rate is relatively intuitive and can give a holistic view about activity levels in a network, but its estimation isn`t easy. Inspired by event detection algorithms, this study proposes an alternative measure, social event magnitude, by using the product of background influence and cooperation value. Background influence is extracted via non-backtracking matrices, and cooperation value is obtained via engagement capacities. This alternative measure does not just integrate multisource information, but also gives a holistic view about activity levels in a network. Social event magnitudes follow a long-tailed distribution; they can be visualized for changing activities and can be applied to online event detections.
關聯:	Proceedings of the Ninth ACM International Conference on Web Intelligence, Mining, and Semantics, Chung-Ang University, pp.Article No. 11
資料類型:	conference
DOI 連結:	https://doi.org/10.1145/3326467.3326481
DOI:	10.1145/3326467.3326481
顯示於類別:	[資訊科學系] 會議論文

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