Mining opinion leaders in big social network

Yi Cheng Chen; Yi Hsiang Chen; Chia Hao Hsu; Hao Jun You; Jianquan Liu; Xin HUANG

doi:10.1109/AINA.2017.147

Mining opinion leaders in big social network

Yi Cheng Chen, Yi Hsiang Chen, Chia Hao Hsu, Hao Jun You, Jianquan Liu, Xin HUANG

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

15 Citations (Scopus)

Abstract

Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.

Original language	English
Title of host publication	Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Editors	Tomoya Enokido, Hui-Huang Hsu, Chi-Yi Lin, Makoto Takizawa, Leonard Barolli
Publisher	IEEE
Pages	1012-1018
Number of pages	7
ISBN (Electronic)	9781509060283
DOIs	https://doi.org/10.1109/AINA.2017.147
Publication status	Published - 5 May 2017
Event	31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 - Taipei, Taiwan, Province of China Duration: 27 Mar 2017 → 29 Mar 2017

Publication series

Name	Proceedings - International Conference on Advanced Information Networking and Applications, AINA
ISSN (Print)	1550-445X

Conference

Conference	31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Country/Territory	Taiwan, Province of China
City	Taipei
Period	27/03/17 → 29/03/17

Scopus Subject Areas

Engineering(all)

User-Defined Keywords

Clustering
Opinion leader
Semantic analysis
Social network

Access to Document

10.1109/AINA.2017.147

Cite this

Chen, Y. C., Chen, Y. H., Hsu, C. H., You, H. J., Liu, J., & HUANG, X. (2017). Mining opinion leaders in big social network. In T. Enokido, H.-H. Hsu, C.-Y. Lin, M. Takizawa, & L. Barolli (Eds.), Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 (pp. 1012-1018). Article 7921017 (Proceedings - International Conference on Advanced Information Networking and Applications, AINA). IEEE. https://doi.org/10.1109/AINA.2017.147

Chen, Yi Cheng ; Chen, Yi Hsiang ; Hsu, Chia Hao et al. / Mining opinion leaders in big social network. Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017. editor / Tomoya Enokido ; Hui-Huang Hsu ; Chi-Yi Lin ; Makoto Takizawa ; Leonard Barolli. IEEE, 2017. pp. 1012-1018 (Proceedings - International Conference on Advanced Information Networking and Applications, AINA).

@inproceedings{a8d3b184cec2426b9131451a4527079c,

title = "Mining opinion leaders in big social network",

abstract = "Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.",

keywords = "Clustering, Opinion leader, Semantic analysis, Social network",

author = "Chen, {Yi Cheng} and Chen, {Yi Hsiang} and Hsu, {Chia Hao} and You, {Hao Jun} and Jianquan Liu and Xin HUANG",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 ; Conference date: 27-03-2017 Through 29-03-2017",

year = "2017",

month = may,

day = "5",

doi = "10.1109/AINA.2017.147",

language = "English",

series = "Proceedings - International Conference on Advanced Information Networking and Applications, AINA",

publisher = "IEEE",

pages = "1012--1018",

editor = "Tomoya Enokido and Hui-Huang Hsu and Chi-Yi Lin and Makoto Takizawa and Leonard Barolli",

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Chen, YC, Chen, YH, Hsu, CH, You, HJ, Liu, J & HUANG, X 2017, Mining opinion leaders in big social network. in T Enokido, H-H Hsu, C-Y Lin, M Takizawa & L Barolli (eds), Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017., 7921017, Proceedings - International Conference on Advanced Information Networking and Applications, AINA, IEEE, pp. 1012-1018, 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017, Taipei, Taiwan, Province of China, 27/03/17. https://doi.org/10.1109/AINA.2017.147

Mining opinion leaders in big social network. / Chen, Yi Cheng; Chen, Yi Hsiang; Hsu, Chia Hao et al.
Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017. ed. / Tomoya Enokido; Hui-Huang Hsu; Chi-Yi Lin; Makoto Takizawa; Leonard Barolli. IEEE, 2017. p. 1012-1018 7921017 (Proceedings - International Conference on Advanced Information Networking and Applications, AINA).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Mining opinion leaders in big social network

AU - Chen, Yi Cheng

AU - Chen, Yi Hsiang

AU - Hsu, Chia Hao

AU - You, Hao Jun

AU - Liu, Jianquan

AU - HUANG, Xin

PY - 2017/5/5

Y1 - 2017/5/5

N2 - Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.

AB - Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.

KW - Clustering

KW - Opinion leader

KW - Semantic analysis

KW - Social network

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T3 - Proceedings - International Conference on Advanced Information Networking and Applications, AINA

SP - 1012

EP - 1018

BT - Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017

A2 - Enokido, Tomoya

A2 - Hsu, Hui-Huang

A2 - Lin, Chi-Yi

A2 - Takizawa, Makoto

A2 - Barolli, Leonard

PB - IEEE

T2 - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017

Y2 - 27 March 2017 through 29 March 2017

ER -

Chen YC, Chen YH, Hsu CH, You HJ, Liu J, HUANG X. Mining opinion leaders in big social network. In Enokido T, Hsu HH, Lin CY, Takizawa M, Barolli L, editors, Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017. IEEE. 2017. p. 1012-1018. 7921017. (Proceedings - International Conference on Advanced Information Networking and Applications, AINA). doi: 10.1109/AINA.2017.147

Mining opinion leaders in big social network

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Scopus Subject Areas

User-Defined Keywords

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