文献类型：期刊文献

英文题名：Potential social media influencers discrimination for concept marketing in online brand community

作者：Li, Shugang[1];Wang, Ru[1];Zhang, Yuqi[1];Lu, Hanyu[1];Cai, Nannan[1];Yu, Zhaoxu[2]

机构：[1]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai, Peoples R China

年份：2021

卷号：41

期号：1

起止页码：317

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：;EI(收录号：20213410805846);WOS:【SSCI(收录号:WOS:000685896700020)，SCI-EXPANDED(收录号:WOS:000685896700020)】；

基金：This work is supported by the National Natural Science Foundation of China (No. 71871135).

语种：英文

外文关键词：Concept marketing; social media influencers; attention concentration index; consensus smart discriminant algorithm

摘要：Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands the users' AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter.