Research on Text Classification for Identifying Fake News In the big data environment, massive news can always lead people to make their own judgments about events happening in society. The wrong guidance of fake news will lead to a negative effect on society. It is necessary to distinguish between real and fake news. In the traditional text categorization method, using the TFIDF information of the words in the document as the weight matrix and applying it to the classifier. Inevitably, TFIDF contains limited information, limiting the effect of classification. This paper proposed a method based on TFIDF and Word2vec for identifying fake news, using SVM to verify its validity.

Towards automatically filtering fake news in Portuguese Abstract   In the last years, the popularity of smartphones and social networks has been contributing to the spread of fake news. Through these electronic media, this type of news can deceive thousands of people in a short time and cause great harm to individuals, companies, or society. Fake news has the potential to change a political scenario, to contribute to the spread of diseases, and even to cause deaths. Despite the efforts of several studies on fake news detection, most of them only cover English language news. There is a lack of labeled datasets of fake news in other languages and, moreover, important questions still remain open. For example, there is no consensus on what are the best classification strategies and sets of features to be used for automatic fake news detection. To answer this and other important open questions, we present a new public and real dataset of labeled true and fake news in Portuguese, and we perform a comprehensive analysis of machine learning methods for fake news detection. The experiments were performed using different sets of features and employing different types of classification methods. A careful analysis of the results provided sufficient evidence to respond appropriately to the open questions. The various evaluated scenarios and the drawn conclusions from the results shed light on the potentiality of the methods and on the challenges that fake news detection presents.