Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.
Wen GU
Japan Advanced Institute of Science and Technology
Shohei KATO
Nagoya Institute of Technology
Fenghui REN
University of Wollongong
Guoxin SU
University of Wollongong
Takayuki ITO
Kyoto University
Shinobu HASEGAWA
Japan Advanced Institute of Science and Technology
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Wen GU, Shohei KATO, Fenghui REN, Guoxin SU, Takayuki ITO, Shinobu HASEGAWA, "Influence Propagation Based Influencer Detection in Online Forum" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 4, pp. 433-442, April 2023, doi: 10.1587/transinf.2022IIP0010.
Abstract: Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022IIP0010/_p
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@ARTICLE{e106-d_4_433,
author={Wen GU, Shohei KATO, Fenghui REN, Guoxin SU, Takayuki ITO, Shinobu HASEGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Influence Propagation Based Influencer Detection in Online Forum},
year={2023},
volume={E106-D},
number={4},
pages={433-442},
abstract={Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.},
keywords={},
doi={10.1587/transinf.2022IIP0010},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - Influence Propagation Based Influencer Detection in Online Forum
T2 - IEICE TRANSACTIONS on Information
SP - 433
EP - 442
AU - Wen GU
AU - Shohei KATO
AU - Fenghui REN
AU - Guoxin SU
AU - Takayuki ITO
AU - Shinobu HASEGAWA
PY - 2023
DO - 10.1587/transinf.2022IIP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2023
AB - Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.
ER -