基于事件表示的虚假情报检测研究

Research on false information detection based on event representation

随着互联网的兴起,虚假情报的广泛传播给社会舆论治理和情报分析带来了困难,准确地分辨虚假情报能够帮助相关部门和人员有针对性地进行处理。为了提高虚假情报检测的效率,提出了基于事件表示的虚假情报检测方法。首先,收集情报文本,并对其进行预处理操作;其次将收集到的情报文本转化成词向量;然后,通过LSTM层捕捉情报文本深层次的语义特征;接着使用全连接层,目的在于将高维特征嵌入到低维向量空间,从而获得情报文本的最终表示;最终,将分类结果反馈给相关情报人员进行鉴别处理。经在谣言数据集上的验证表明,该方法可以较好地区分谣言事件与非谣言事件,为更精准地实现情报鉴别提供支持。

With the rise of the Internet,the widespread spread of false intelligence has brought difficulties to the governance of public opinion and the analysis of intelligence personnel.Accurately identifying false intelligence can help relevant departments and personnel deal with it in a targeted manner.In order to improve the efficiency of false information detection,this study proposes a false information detection method based on event representation.Firstly,the information text is collected and preprocessed.Secondly,the collected information text is transformed into word vector.Secondly the deep semantic features of the information text are captured by LSTM layer.Then the full connection layer is used to embed the high-dimensional features into the low-dimensional vector space,so as to obtain the final representation of the information text.Finally,the classification results are fed back to the relevant intelligence personnel for identification.The verification on micro-blog rumor datasets shows that the proposed method can better distinguish rumor events from non-rumor events,which proves that the proposed method can provide support for more accurate intelligence identification.