大数据背景下的恐怖主义信息传播途径分析

为加强对互联网以及社交媒体的恐怖主义信息的甄别和处理，利用数据的爬取和分析甄别，分类恐怖主义信息。通过scrappy 框架以及gensim 工具包对数据进行爬取，得到了4 个数据集，并且进行LDA( Latent Dirichlet Allocation) 主题模型的建立，分别得到恐怖主义信息传播在4 个不同领域中的主题分类，以及主题间的关系。实验表明，通过爬取可以有效鉴别出不同的恐怖主义信息主题，以及其在传播中的作用。

Analysis on Spread of Terrorism through Media in Era of Big Data

Improving screening and addressing terrorist information in social media becomes a critical topic of research． Our study uses data crawlers and screening analysis to classify different terrorist information． Through scrappy framework and genism tool pack，we obtained the relevant data from four datasets． After constructing a LDA ( Latent Dirichlet Allocation) theme model，we then classified several themes in four different area that played major roles in propagating terrorism，and investigated the interconnected relationship between these themes． The experiment indicates that the model can effectively distinguish different themes of terrorism information and the role it played in propagation．