Toward estimating user-social event distance: Mobility, content, and social relationship

Fei Yi, Bin Guo, Zhiwen Yu, Qin Lv

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

On-site user w.r.t social events are valuable, from whom, government/police could obtain meaningful information which contributes to understand the progress of the event or investigate suspects when the event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns, it is hard to identify on-site users while social event happens. In this paper, we propose a Fused fEature Gaussian prOcess Rgression (FEGOR) model, which employs three features from online social networks: mobility influence, content similarity, and social relationship to estimate the distance between user and social event, based on which, we could accomplish the problem of identifying the on-site users. Experiment results on a realworld Twitter dataset demonstrate our method outperforms state-of-The-Art methods.

源语言英语
主期刊名UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
出版商Association for Computing Machinery, Inc
233-236
页数4
ISBN(电子版)9781450344623
DOI
出版状态已出版 - 12 9月 2016
活动2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, 德国
期限: 12 9月 201616 9月 2016

出版系列

姓名UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

会议

会议2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
国家/地区德国
Heidelberg
时期12/09/1616/09/16

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