论文标题
通过自然语言处理使用机器学习来开发假新闻模型
Development of Fake News Model using Machine Learning through Natural Language Processing
论文作者
论文摘要
假新闻检测研究仍处于早期阶段，因为这是社会提出的相对较新的现象。机器学习有助于解决复杂的问题并建立AI系统，尤其是在我们拥有默认知识或知识知识的情况下。我们使用机器学习算法并识别假新闻；我们应用了三个分类器；被动攻击性，幼稚的贝叶斯和支撑向量机。在假新闻检测中，简单的分类不是完全正确的，因为分类方法并非专门用于假新闻。通过集成机器学习和基于文本的处理，我们可以检测到假新闻并构建可以对新闻数据进行分类的分类器。文本分类主要集中于提取文本的各种特征，然后将这些特征纳入分类。在这一领域，最大的挑战是由于语料库的不可用而缺乏有效的方法来区分假和非伪造的方法。我们在两个公开可用的数据集上应用了三个不同的机器学习分类器。基于现有数据集的实验分析表明性能非常令人鼓舞和改善。
Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.
