FakeNewsSetGen: um processo para construção de datasets que viabilizem a comparação entre métodos de detecção de Fake News baseados em diferentes demandas de informação

Abstract

Due to easy access and low cost, social media online news consumption has increased significantly for the last decade. Despite their benefits, some social media allow anyone to post news with intense spreading power, which amplifies an old problem: the dissemination of fake news (ie., false information that is spread deliberately to deceive). The proliferation of fake news generally affects not only journalistic integrity, but also disrupts social, political, economic, cultural, as well as health and safety. In the face of this scenario, several machine learning-based methods to automatically detect fake news (MLFN) have been proposed. All of them require fake news datasets to train and evaluate their detection models. Although recent MLFN were designed to consider data regarding the news propagation on social media, most of the few available fake news datasets do not contain this kind of data. Hence, comparing the performances amid those recent MLFN and the others is restricted to a very limited number of datasets. Moreover, all existing datasets with propagation data do not contain news in Portuguese, which impairs the evaluation of the MLFN in this language. Thus, this work proposes FakeNewsSetGen, a process that builds fake news datasets that contain news propagation data and support comparison amid the state-of-the-art MLFN. FakeNewsSetGen’s software engineering process was guided to include all kind of data required by the existing MLFN. In order to illustrate FakeNewsSetGen’s viability and adequacy, a case study was carried out. It encompassed the implementation of a FakeNewsSetGen prototype and the application of this prototype to create a dataset called FakeNewsSet, with news in Portuguese. Ten MLFN with different kind of data requirements (seven of them demanding news propagation data) were applied to FakeNewsSet and compared, demonstrating the potential use of both the proposed process and the created dataset.