标题:Effective and effortless features for popularity prediction in microblogging network
作者:Gao, Shuai ;Ma, Jun ;Chen, Zhumin
作者机构:[Gao, Shuai ;Ma, Jun ;Chen, Zhumin ] School of Computer Science and Technology, Shandong University, Jinan; 250101, China
会议名称:23rd International Conference on World Wide Web, WWW 2014
会议日期:7 April 2014 through 11 April 2014
来源:WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
出版年:2014
页码:269-270
DOI:10.1145/2567948.2577312
关键词:Classification; Microblogging; Popularity prediction; Social network; Temporal features
摘要:Predicting popularity of online contents is of remarkable practical value in various business and administrative applications. Existing studies mainly focus on finding the most effective features for prediction. However, some effective features, such as structural features which are extracted from the underlying user network, are hard to access. In this paper, we aim to identify features that are both effective and effortless (easy to obtain or compute). Experiments on Sina Weibo show the effectiveness and effortlessness of the temporal features and satisfying prediction performance can be obtained based on only the temporal features of first 10 retweets. © Copyright 2014 by the International World Wide Web Conferences Steering Committee.
收录类别:EI;SCOPUS
Scopus被引频次:6
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990877547&doi=10.1145%2f2567948.2577312&partnerID=40&md5=fa705431654d35ac2c41857245e61e12
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