基于行为分析的微博信息传播效果

微博的传播效果研究对于提高市场营销效率、加强舆情监控和准确发现热点具有重要作用。针对以前传播效果研究中未考虑用户个体差异的问题，提出一种基于行为分析的微博转发规模和传播深度预测方法。从微博用户自身、用户关系和微博内容3个方面提取9个相关特征，结合逻辑回归（LR）方法提出一种转发行为预测模型，并基于此模型结合信息沿用户传播特点，通过逐级对相邻用户迭代统计分析得到转发规模和传播深度预测方法。在新浪微博数据集上的实验结果表明，所提方法对转发规模和传播深度预测的正确率分别约为87.1%和81.6%，能较好地预测出信息传播效果。

Micro-blog information diffusion effect based on behavior analysis

The research of dissemination effect of micro-blog message has an important role in improving marketing, strengthening public opinion monitoring and discovering hotspots accurately. Focused on difference between individuals which was not considered previously, this paper proposed a method of predicting scale and depth of retweeting based on behavior analysis. This paper presented a predictive model of retweet behavior with Logistic Regression (LR) algorithm and extracted nine relative features from users, relationship and content. Based on this model, this paper proposed the above predicting method which considered the character of information disseminating along users and iterative statistical analysis of adjacent users step by step. The experimental results on Sina micro-blog dataset show that the accuracy rate of scale and depth prediction approximates 87.1% and 81.6 respectively, which can predict the dissemination effect well.