Fake News Detection for P ortuguese with Deep Learning (2021)

Abstract: The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term “fake news” became increasingly popular and this phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.

## introduction the term "fake news" is relatively new, having emerged in the 19 th century. before the 18 th century the word "fake" was seldom used as an adjective and the expression "false news" was more common. according to google trends, the search for the term has significantly increased since october/november 2016, coinciding with the 2016 us presidential elections. even though the term gained popularity only in the past few years, the phenomenon of fake news itself is not new. misinformation, disinformation, propaganda, conspiracy theories and others have always existed. however, the spread of this kind of content has exponentially grown with the new communication technology. as a result of the rapid growth of the internet in the past decades, as well as the growth of social media, false or misleading information have been spreading at an alarming rate. social media introduced a new kind of public space, one where every individual has the opportunity to voice their opinion and potentially be heard by any other individual with internet access. the growth of social media and the lack of control of online content are major contributing factors to the phenomenon of fake news. nowadays any person or organisation can create a social media profile and disguise as a professional news outlet. these actors provide misleading information which pretends to be reliable news. fake news also tend to be more appealing than true stories. tweets containing false information reach 1500 persons six times faster than tweets containing real information @xcite . according to garner's prediction, "by 2022 most people in mature economies will consume more false information than true information" @xcite . several fact-checking agencies have surged as a response to the growth in fake news dissemination. however, manual checking of news is clearly insufficient when we consider the volume of posts in any of the major social media platforms @xcite . only twitter, for example, had 340 million users and 500 million tweets per day on december 2020 @xcite . while most work on fake news detection has been done for english, the topic has been scarcely researched for portuguese and this ongoing study seeks to fill in this gap. our objective is not only to develop additional fake detection tools for portuguese, but to employ latest deep learning techniques in order to establish whether they will enhance the state-of-the-art of fake detection for portuguese. the rest of the paper is structured as follows. section 2 surveys the work on fake detection for portuguese so far. section 3 of this work-in-progress poster paper outlines the envisaged methodology and briefly touches on the planned evaluation. ## related work reis et al. @xcite analysed several classification algorithms that use supervised learning. they use a dataset consisting of 2282 buzzfeed articles about the 2016 us presidential election labelled by journalists. the authors explain three types of information can be extracted from news: content features, source features and environmental features, and extract several features of each of these categories. several classification algorithms were selected: k-nearest neighbours (knn), naive bayes (nb), random forests (rf), support vector machine with rbf kernel (svm), and xgboost (xgb). these models were used to evaluate the discrimination power of the extracted features. while it would have been promising to experiment with deep learning techniques, the authors opted for hand crafted features. the manual extraction of these features is probably time consuming, even with the help of automatic tools. silva et al. @xcite evaluated different detection methods on the fake.br dataset @xcite , a dataset of fake news in portuguese. the authors go about their study in two different ways: by extracting linguistic features from the data and by employing vector representations. they chose three types of vector representations: bag-ofwords with tf-idf, word2vec and fast text, and used pre-trained word2vec and fast text vectors. the idea was to compare what would perform better: hand-crafted features or automatically extracted features. several classification algorithms were selected in order to evaluate the representations: logistic regression (lr), support vector machines (svm), decision trees (dt), random forest (rf), bootstrap aggregating (bagging) and adaptive boosting (ada-boost). the experiments with linguistic based features showed that these features are good enough to detect more than 90% of the fake news. this result is very interesting, because hand-crafted features do not require complex classifiers, meaning that a detection algorithm could run on even the simplest of devices. in the experiments with vector representations, the authors conclude that the bag-of-words model delivered better results than fast text or word2vec. the best f-measure obtained with word2vec and fasttext were 0.893 and 0.897, respectively, while the best f-measure with bow was 0.971. the authors hypothesise that, since the pre-trained vectors were trained on well-written texts such as wikipedia and google news, these vectors were not the best fit to represent fake news, which contains noise, such as incorrect spelling and slang.