Sylvia Iasulaitis, Eloize Rossi Marques Seno, Mariana Caravanti Souza, Alan Demétrius Baria Valejo, Isabella Vicari, Ian Victor Rubini Ruiz, Yanni Marcela Gameiro, Eanes Torres Pereira, Guilherme Henrique Messias Messias, Bruno Cardoso Greco, Rafaela de Amorim Barbosa Silva

FactPolCheckBr: a dataset of fake news fact-checked during the 2022 Brazilian presidential elections

Introduction

Factpolcheckbr: a dataset of fake news fact-checked during the 2022 brazilian presidential elections . FactPolCheckBr is a new dataset of 1,873 fact-checked fake news items from the 2022 Brazilian presidential elections. Aids researchers in developing computational models for disinformation detection in Brazilian Portuguese.

Abstract

The use of social media and instant messaging applications in contemporary society has amplified the dissemination of misleading content, reaching audiences on an unprecedented scale. Many news outlets have made considerable efforts to check the accuracy of online content, especially during election periods, which are critical moments for spreading fake news. However, the fake news verification process is labor-intensive for humans, given the volume and speed at which misinformation circulates. Numerous studies in natural language processing and machine learning have emerged in recent years seeking to investigate and develop computational models capable of detecting fake news. Algorithm training is primarily based on supervised machine learning, which relies on labeled datasets to learn the characteristic patterns of misinformation. Labeled fake news datasets in Brazilian Portuguese are scarce. This research addresses this gap developing the first fact-checked fake news dataset related to the 2022 presidential elections in Brazil, which was widely regarded as the most polarized in the country’s political history and marked by a large-scale disinformation campaign. The dataset, called FactPolCheckBr, includes 1,873 news items categorized as fake news, which were manually collected from online fact-checking platforms. The full texts of the fake news items were subsequently retrieved from the web using a scraping algorithm. Next, a clustering algorithm was applied to group similar news items, which enabled the identification of the main topics targeted by fake news during the elections. Each news item in the dataset also includes information on the candidate favored by the misinformation in that electoral context. The information was provided by political scientists who employed content analysis to examine the news texts carefully. This article presents an exploratory study of the FactPolCheckBr dataset, highlighting its key features and potential applications across various domains.

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