基于个人属性特征的微博用户影响力分析

为提高微博话题中关键人识别的准确性, 提出了一种基于个人属性特征的用户影响力分析方法——PBF方法。该方法利用信息传播特征对用户影响力进行度量, 结合个人属性特征对其进行回归分析, 找出最能反映用户影响力的属性特征, 进而利用这些特征对用户影响力进行预测。实验结果表明, PBF方法的识别效率要明显高于RNF方法, 有效提高了关键人识别的准确性。

Analysis of user influence in microblog based on individual attribute features

To improve the accuracy of key-person recognition in microblog topics, this paper proposed a new method of user influence analysis named PBF which based on individual attribute features. The method firstly used information diffusion characters to measure user influence, and then made regression analysis with individual attribute features to find out the ones effecting user influence most, with which predicted user influence. The experimental results indicate that the recognition efficiency of PBF method is obviously superior to RNF, improving the accuracy of key-person recognition effectively.