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

Fei Yi; Bin Guo; Zhiwen Yu; Qin Lv

doi:10.1145/2968219.2971430

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

Fei Yi
, Bin Guo
, Zhiwen Yu
, Qin Lv

School of Computer Science

Northwestern Polytechnical University Xian
University of Colorado Boulder

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Title of host publication	UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	233-236
Number of pages	4
ISBN (Electronic)	9781450344623
DOIs	https://doi.org/10.1145/2968219.2971430
State	Published - 12 Sep 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: 12 Sep 2016 → 16 Sep 2016

Publication series

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

Conference

Conference	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/Territory	Germany
City	Heidelberg
Period	12/09/16 → 16/09/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Content Similarity
Mobility Influence
On-Site User
Social Relationship
User-Social Event Distance

Access to Document

10.1145/2968219.2971430

Cite this

Yi, F., Guo, B., Yu, Z., & Lv, Q. (2016). Toward estimating user-social event distance: Mobility, content, and social relationship. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 233-236). (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971430

Yi, Fei ; Guo, Bin ; Yu, Zhiwen et al. / Toward estimating user-social event distance : Mobility, content, and social relationship. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 233-236 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

@inproceedings{e5a27e4c67a94177a920ce60fb834b52,

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

abstract = "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.",

keywords = "Content Similarity, Mobility Influence, On-Site User, Social Relationship, User-Social Event Distance",

author = "Fei Yi and Bin Guo and Zhiwen Yu and Qin Lv",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

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day = "12",

doi = "10.1145/2968219.2971430",

language = "英语",

series = "UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

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Yi, F, Guo, B, Yu, Z & Lv, Q 2016, Toward estimating user-social event distance: Mobility, content, and social relationship. in UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 233-236, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 12/09/16. https://doi.org/10.1145/2968219.2971430

Toward estimating user-social event distance: Mobility, content, and social relationship. / Yi, Fei; Guo, Bin; Yu, Zhiwen et al.
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 233-236 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Toward estimating user-social event distance

T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016

AU - Yi, Fei

AU - Guo, Bin

AU - Yu, Zhiwen

AU - Lv, Qin

PY - 2016/9/12

Y1 - 2016/9/12

N2 - 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.

AB - 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.

KW - Content Similarity

KW - Mobility Influence

KW - On-Site User

KW - Social Relationship

KW - User-Social Event Distance

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U2 - 10.1145/2968219.2971430

DO - 10.1145/2968219.2971430

M3 - 会议稿件

AN - SCOPUS:84991109258

T3 - UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

SP - 233

EP - 236

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

PB - Association for Computing Machinery, Inc

Y2 - 12 September 2016 through 16 September 2016

ER -

Yi F, Guo B, Yu Z, Lv Q. Toward estimating user-social event distance: Mobility, content, and social relationship. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 233-236. (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2968219.2971430

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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