基于神经网络的微博虚假消息识别模型

A Rumor Detection Model Based on Neural Network

文章提出一种融合博主属性和微博消息文本特征的神经网络识别模型。首先根据博主信息获取博主特征:博主类别、博主活跃度、博主好友值、博主粉丝值、博主消息原创比;接着基于word2vec得到微博文本表征向量,并利用并行卷积人工神经网络提取文本特征,根据语义内容得到文本的特征表示;最后构建BP神经网络识别模型,融合微博文本的语义属性和微博博主的自身属性,作为识别模型的多元特征输入,利用BP神经网络识别微博消息。

This paper proposes a blogger and micro-blog fusion attribute message text feature neural network recognition model. First, according to characteristics of blogger information: blogger category, blogger activity, blogger friends, fans, bloggers original news.Then micro-blog text representation vectors based on word2vec, and extracted the text feature by parallel convolutional neural network, according to the semantic content of text feature representation.Finally building BP neural network rumor detection model, the integration of micro-blog text semantic and micro-blog blogger as the model's multi-feature to identify micro-blog news.