基于循环神经网络的微博转发行为预测

随着网络的飞速发展，微博逐渐成为社交网络中信息传播及信息收集的重要平台，微博转发是微博信息传播的重要途径，研究微博转发问题对微博信息传播、微博营销、舆情监控有着极其重要的意义.影响微博转发的主要因素有：粉丝兴趣与微博文本的相似度，微博营销策略及用户粉丝数量的变化.以往的预测模型没有综合考虑这两方面因素，基于此，提出了一种基于循环神经网络的方法来对微博转发量级进行预测，首先利用SIM-LSTM模型构建微博转发趋势度，然后再利用TF-IDF构建粉丝兴趣和微博文本的相似度，最后通过神经网络模型来预测粉丝是否会转发该微博.实验结果表明本文提出的算法相对于其他预测算法F1评估值提高了近5%.

Weibo Forwarding Behavior Prediction by Deep Recurrent Neural Network

With the rapid development of the Internet, the Weibo has gradually become an important way of information dissemination and information collection in social communication, and Weibo retweeting is an important way to spread information on Weibo.The study of the Weibo retweeting problem has a very important significance to Weibo communication, Weibo marketing, and public opinion monitoring. The main factors affecting the retweeting of Weibo are similarity between followers'' interest and Weibo text, and changes in Weibo marketing strategy and number of user followers. The previous forecasting models did not consider these two factors comprehensively. To solve the above mentioned problem, this study proposes a method based on recurrent neural network to predict magnitude of Weibo retweeting. First, the SIM-LSTM model is used to build the trend of Weibo retweeting. Then, TF-IDF is used to build the similarity between followers'' interest and Weibo text. And finally, neural network model is used to predict whether followers will forward the Weibo. the experiments show that the F1 evaluation value using the proposed algorithm is increased by 5% comparing with other traditional prediction methods.