预测信息传播中的转发选择

在信息传播中,用户在重复接收同一信息的情况下其转发行为会具有一定的倾向性。对这种转发的倾向性建模是影响力分析、传播动力学、社会推荐等一系列信息传播相关应用研究领域中的一个关键问题。本文假设用户的转发选择行为主要由用户间的人际影响力决定。人际影响力的大小由信息传播者的影响力和信息接收者的易感性共同作用。本文从真实的信息传播记录中推断出用户隐式的影响力和易感性,进而提出了一种转发选择模型。该模型能够有效解决目前方法存在的对转发选择行为建模不充分和模型泛化能力差的问题。本文选取典型的转发选择建模方法作为比较,将所提的转发选择模型在新浪微博数据上进行对比验证。实验表明,本文所提的模型在两种评价指标上均取得更好效果,证明了所提模型的有效性。

Predicting Forwarding Preference in Information Propagation

In information propagation, users have forwarding preference when receiving same message repeatedly. Modeling forwarding preference is fundamental to information propagation and other related applications, e.g., influence analytics, cascade dynamics and social recommendation. In this paper, we suggest forwarding preference is mainly affected by interpersonal influence, determined by both influence and susceptibility from the sender and the receiver, respectively. We propose to model such user-specific latent influence and susceptibility by the Forwarding Preference Model. We compare our proposed model with state-of-the-art forwarding preference models on the dataset from Weibo, which demonstrates that the proposed model consistently outperforms other methods at two evaluation measures.