文献类型：期刊文献

英文题名：Predicting the influence of viral message for VM campaign on Weibo

作者：Li Shugang[1];Zhang Yuqi[1];Yu Zhaoxu[2];Zhang Fangfang[1];Lu Hanyu[1]

机构：[1]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China

年份：2019

卷号：36

外文期刊名：ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS

收录：;EI(收录号：20193007224752);WOS:【SSCI(收录号:WOS:000477669200014)，SCI-EXPANDED(收录号:WOS:000477669200014)】；

基金：This work was supported by the Chinese National Natural Science Foundation (Nos. 71871135, 71271132).

语种：英文

外文关键词：Weibo; Viral marketing; Seeding strategy; FEM

摘要：Viral marketing (VM) method has the benefits of rapid dissemination and is easy to reach the "hard-to-get" audience, consequently, it has irritated the interest of academics and entrepreneurs. On social network sites, the prerequisite for successfully launching an effective VM campaign is to accurately predict the influence of viral message (IVM) under various seeding strategies that initiate and promote the campaign. However, in current prediction models of IVM, the effect of seeds on viral messages dissemination is ideally assumed to be constant and the corresponding seeding strategies are ignored. In this study, an IVM prediction model is proposed by taking the seeds' influence with respect to infecting their followers into consideration. The attention features of seeding strategies, which get users to be attentive to a viral message, are identified and the flexible ensemble model (FEM) is developed to estimate seed's influence under various seeding strategies. FEM evaluates the generalization ability of prediction models by generalization performance index and adaptively selects the appropriate model with the highest similarity to other models that perform well in generalization using rough similarity index. The proposed models are assessed with the real VM cases on Weibo and the experiment results show that FEM can offset the performance variability of traditional data mining models in different conversion volume samples and has strong robustness and adaptability.