Topical influential user analysis with relationship strength estimation in Twitter

Xinyue Liu; Hua Shen; Fenglong Ma; Wenxin Liang

doi:10.1109/ICDMW.2014.11

Topical influential user analysis with relationship strength estimation in Twitter

Xinyue Liu, Hua Shen, Fenglong Ma, Wenxin Liang

College of Information Sciences and Technology

Research output: Contribution to journal › Conference article › peer-review

19 Scopus citations

Abstract

Topical Influential User Analysis (TIUA) is an important technique in Twitter. Existing techniques neglected relationship strength between users, which is a crucial aspect for TIUA. For modeling relationship strength, interaction frequency between users has not been considered in previous works. In this paper, we firstly introduce a poisson regression-based latent variable model to estimate relationship strength by utilizing interaction frequency. We then propose a novel TIUA framework which uses not only retweeting relationship but also relationship strength. Experimental results show that the proposed TIUA algorithm can greatly improve the precision and relevance on finding topical influential users in Twitter.

Original language	English (US)
Article number	7022707
Pages (from-to)	1012-1019
Number of pages	8
Journal	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2015-January
Issue number	January
DOIs	https://doi.org/10.1109/ICDMW.2014.11
State	Published - Jan 26 2015
Event	14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China Duration: Dec 14 2014 → …

All Science Journal Classification (ASJC) codes

Computer Science Applications
Software

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10.1109/ICDMW.2014.11

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title = "Topical influential user analysis with relationship strength estimation in Twitter",

abstract = "Topical Influential User Analysis (TIUA) is an important technique in Twitter. Existing techniques neglected relationship strength between users, which is a crucial aspect for TIUA. For modeling relationship strength, interaction frequency between users has not been considered in previous works. In this paper, we firstly introduce a poisson regression-based latent variable model to estimate relationship strength by utilizing interaction frequency. We then propose a novel TIUA framework which uses not only retweeting relationship but also relationship strength. Experimental results show that the proposed TIUA algorithm can greatly improve the precision and relevance on finding topical influential users in Twitter.",

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AU - Liu, Xinyue

AU - Shen, Hua

AU - Ma, Fenglong

AU - Liang, Wenxin

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N2 - Topical Influential User Analysis (TIUA) is an important technique in Twitter. Existing techniques neglected relationship strength between users, which is a crucial aspect for TIUA. For modeling relationship strength, interaction frequency between users has not been considered in previous works. In this paper, we firstly introduce a poisson regression-based latent variable model to estimate relationship strength by utilizing interaction frequency. We then propose a novel TIUA framework which uses not only retweeting relationship but also relationship strength. Experimental results show that the proposed TIUA algorithm can greatly improve the precision and relevance on finding topical influential users in Twitter.

AB - Topical Influential User Analysis (TIUA) is an important technique in Twitter. Existing techniques neglected relationship strength between users, which is a crucial aspect for TIUA. For modeling relationship strength, interaction frequency between users has not been considered in previous works. In this paper, we firstly introduce a poisson regression-based latent variable model to estimate relationship strength by utilizing interaction frequency. We then propose a novel TIUA framework which uses not only retweeting relationship but also relationship strength. Experimental results show that the proposed TIUA algorithm can greatly improve the precision and relevance on finding topical influential users in Twitter.

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M3 - Conference article

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JO - IEEE International Conference on Data Mining Workshops, ICDMW

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ER -

Topical influential user analysis with relationship strength estimation in Twitter

Abstract

All Science Journal Classification (ASJC) codes

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