Nowadays social media is one of the important medium of sharing thoughts and opinions of the individual due to its easy access and also it provides an opportunity to the malicious user to post deliberately fabricated false content to influence people for creating controversies, playing with public emotions, etc. The spread of contaminated information such as Rumours, Hoax, Accidental misinformation, etc. over the web is becoming an emergency situation that can have a very harmful impact on society and individuals. In this paper, we have developed an automated system “Hoax-News Inspector” for the detection of fake news that propagates through the web and social media in the form of text. To distinguish fake and real reports on an early basis, we identified prominent features by exploring two sets of attributes that lead to information spread: Article/post-content-based features, Sentiment based features and the mixture of both called as Hybrid features. The proposed algorithm is trained and tested on the self-generated dataset as well as one of the popular existing datasets Liar. It has been found that the proposed algorithm gives the best results using the Random Forest classifier with an accuracy of 95% by considering all sets of features. Detecting and verifying news have many practical applications for business markets, news consumers, and time-sensitive services, which generally help to minimize the spread of false information. Our proposed system Hoax News-Inspector can automatically collect fabricated news data and classify it into binary classes Fake or Real, which later benefits further research for predicting and understanding Fake news.

如今，社交媒体由于易于访问而成为分享个人思想和观念的重要媒介之一，它还为恶意用户提供了发布故意捏造的虚假内容的机会，以影响人们制造争议，玩弄公共情感，诸如谣言，恶作剧，意外错误信息等受污染信息在网络上的传播正成为一种紧急情况，可能对社会和个人造成非常有害的影响。在本文中，我们开发了一个自动化系统“ Hoax-News Inspector”，用于检测以文本形式通过网络和社交媒体传播的虚假新闻。为了及早区分虚假和真实的报告，我们通过探索导致信息传播的两套属性来确定突出的特征：基于文章/内容的功能，基于情感的功能以及两者的混合称为混合功能。在自生成数据集以及流行的现有数据集之一上对提出的算法进行了训练和测试骗子。已经发现，通过考虑所有特征集，提出的算法使用随机森林分类器以95％的精度提供最佳结果。检测和验证新闻对于商业市场，新闻消费者和对时间敏感的服务具有许多实际应用，通常有助于最大程度地减少虚假信息的传播。我们提出的系统Hoax News-Inspector可以自动收集虚假新闻数据并将其分类为Fake或Real二进制类，这对以后的预测和理解Fake新闻的进一步研究很有帮助。