基于命名实体识别和短语检测的假新闻识别特征

H. Al-Ash, W. Wibowo
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引用次数: 24

摘要

任何人都可以产生的信息爆炸可能导致假新闻不仅在新闻频道传播,而且在社交媒体上传播,等等。由于假新闻的传播引起了社会的不安,对假新闻的检测已成为社会的迫切需要。在新闻分类方面已经进行了一些相关的研究,目的是提供一个新闻是属于假新闻还是原创新闻的决定。在相关研究中,使用了文档的向量表示。然后将该向量表示交给算法进行进一步处理。本研究的目的是在使用印尼语的语言算法进行进一步处理之前,对能够容纳假新闻特征的向量进行建模。在本研究中,假新闻和原创新闻根据向量空间模型表示。使用支持向量机算法对频率项、文件逆频率和频率反10倍交叉验证的向量模型进行分类。短语检测的变体以及名称识别实体(实体识别名称)也用于向量表示。使用术语“频率”的向量表示显示出良好的性能。通过短语检测和命名实体识别过程,对2516个文档的新闻特征识别率达到96.74%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fake News Identification Characteristics Using Named Entity Recognition and Phrase Detection
Information explosion that can be generated by anyone may lead to the spread of fake news not only at the news channel, but also at social media, and so forth. Detection of fake news has become an urgent need on the society because of fake news spread of unrest in the society. Several related studies have been conducted in the news classification with the aim of providing a decision whether a news is included in fake news or original news. In the related research, a vector representation of documents is used. This vector representation is then given to the algorithm for further processing. This study aims to model vectors that can accommodate the characteristics of fake news before further processed by language algorithms using the Indonesian language. In this research, fake news and original news are represented according to the vector space model. Vector model combination of frequency term, inverse document frequency and frequency reversed with 10-fold cross validation using support vector machine algorithm classifier. Variations of phrase detection as well as name recognition entities (entity recognition names) are also used in vector representation. A vector representation that uses the term frequency shows promising performance. It can recognize news characteristics correctly 96.74% of 2516 documents across phrase detection and named entity recognition process.
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