IEICE TRANSACTIONS on Information
InfluenceRank: Trust-Based Influencers Identification Using Social Network Analysis in Q&A Sites
Summary :
Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.9 pp.2343-2346
- Publication Date
- 2012/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2343
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
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GunWoo PARK, SungHoon SEO, SooJin LEE, SangHoon LEE, "InfluenceRank: Trust-Based Influencers Identification Using Social Network Analysis in Q&A Sites" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 9, pp. 2343-2346, September 2012, doi: 10.1587/transinf.E95.D.2343.
Abstract: Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2343/_p
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@ARTICLE{e95-d_9_2343,
author={GunWoo PARK, SungHoon SEO, SooJin LEE, SangHoon LEE, },
journal={IEICE TRANSACTIONS on Information},
title={InfluenceRank: Trust-Based Influencers Identification Using Social Network Analysis in Q&A Sites},
year={2012},
volume={E95-D},
number={9},
pages={2343-2346},
abstract={Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.},
keywords={},
doi={10.1587/transinf.E95.D.2343},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - InfluenceRank: Trust-Based Influencers Identification Using Social Network Analysis in Q&A Sites
T2 - IEICE TRANSACTIONS on Information
SP - 2343
EP - 2346
AU - GunWoo PARK
AU - SungHoon SEO
AU - SooJin LEE
AU - SangHoon LEE
PY - 2012
DO - 10.1587/transinf.E95.D.2343
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2012
AB - Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.
ER -
English
