文献类型：期刊文献

英文题名：Sociability-based Influence Diffusion Probability Model to evaluate influence of BBS post

作者：Li, Lei[1];Lin, Xin[1];Zhou, MengChu[2,3];Fu, Lili[4]

第一作者：Li, Lei

通讯作者：Zhou, MC[1];Zhou, MC[2]

机构：[1]Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China;[2]New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA;[3]Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China;[4]Beijing Union Univ, Business Coll, Beijing 100025, Peoples R China

第一机构：Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China

通讯机构：[1]corresponding author), New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA;[2]corresponding author), Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China.

年份：2018

卷号：293

起止页码：18-28

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20181304944372);Scopus(收录号：2-s2.0-85044326911);WOS:【SSCI(收录号:WOS:000429323200003)，SCI-EXPANDED(收录号:WOS:000429323200003)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 91546121 and 71231002, National Social Science Foundation of China under Grant 16ZDA055; FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant 119/2014/A3, 2017; EU FP7 IRSES MobileCloud Project (Grant No. 612212); the 111 Project of China under Grant B08004; Engineering Research Center of Information Networks, Ministry of Education.

语种：英文

外文关键词：Bulletin Board System (BBS); Post influence; Influence diffusion model; Sociability; Probability model

摘要：A Bulletin Board System (BBS) yields user-generated posts, which has enjoyed fast spreading speed. Significant events are often revealed by a post. It may then spread widely, thereby producing large influence in some specific social circles and sometimes the whole society. Hence, evaluating post influence becomes important. It can help Web service providers locate quickly those influential posts, users or communities, place right advertisements, expand an event's influence, and explode a hot topic's discussions. Recently, BBS has grown to have some new features, e.g., sociability. The existing studies use an Influence Diffusion Model (IDM) and its expanded versions for the analysis of influence. However, they suffer from such drawbacks as identical treatment of every comment or reply, and complete ignorance of relationships among users, thereby leading to the inaccurate assessment of post influence. To overcome the limitations, inspired by our prior user model for user participation in virtual communities, we propose a behavioral model for user participation in a post and give a Sociability-based Influence Diffusion Probability Model (S-IDPM) by utilizing user relationship and reply-chains to measure the responses of different users and evaluate post influence. Experiments with real data collected from a popular BBS. Our results show that S-IDPM outperforms IDM and its expanded version called Influence Diffusion Probability Model (IDPM). S-IDPM can be helpful to achieve better post influence diffusion evaluation than IDM and IDPM do. (c) 2018 Elsevier B.V. All rights reserved.