A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese

Currently, there has been a significant increase in the diffusion of fake news worldwide, especially the political class, where the possible misinformation that can be propagated, appearing at the elections debates around the world. However, news with a recreational purpose, such as satirical news, is often confused with objective fake news. In this work, we decided to address the differences between objectivity and legitimacy of news documents, where each article is treated as belonging to two conceptual classes: objective/satirical and legitimate/fake. Therefore, we propose a DSS (Decision Support System) based on a Text Mining (TM) pipeline with a set of novel textual features using multi-label methods for classifying news articles on these two domains. For this, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared with a multi-class approach. Also, a set of real-life news data was collected from several Brazilian news portals for these experiments. Results obtained reported our DSS as adequate (0.80 f1-score) when addressing the scenario of misleading news, challenging the multi-label perspective, where the multi-class methods (0.01 f1-score) overcome by the proposed method. Moreover, it was analyzed how each stylometric features group used in the experiments influences the result aiming to discover if a particular group is more relevant than others. As a result, it was noted that the complexity group of features could be more relevant than others

Walkability variables: an empirical study in Rolândia - PR, Brazil

The built environment possessed determinants of more active lifestyles, related to social and cultural reality. Thus, relevant walkability variables in large cities and in developed countries may not be suitable for mid-sized Brazilian towns. Therefore, from a case study, the objective of this research was to evaluate the relevance of eight objective walkability variables: Residential Density; Retail Floor Area Ratio; Mixed Land Use (Entropy); Space Syntax - Integration and Choice; Land and Real Estate values in a case study of a mid-sized Brazilian town. From the geocoding of data from the Municipal Urban Mobility Plan, urban form variables were aggregated and tested in 1000 meter network buffers. Analyzes were performed using a machine learning approach, through the Random Forest algorithm, in relation to self-reported walking (meters walked per unit of area). Results indicated that the most relevant characteristics were: Entropy, Integration within a 2000 meter radius and Residential Density. Contributions include the possibility of subsidizing urban planning policies in adopting an evidence-based approach.The built environment is a key determinant of physically active lifestyles. Notwithstanding, as social reality and physical activity are connected (BAUMAN et al., 2012), relevant walkability constructs for larger cities and high-income countries may not be suited for Brazilian cities.  Therefore, the main objective of this research is to evaluate the relevance of individual walkability-built environment features in mid-size Brazilian cities. From the systematizing of spatial data and a subjective database from the Urban Mobility Plan (n=756) of a case study, eight different walkability-related urban form features were aggregated in 1000 meters street network buffers and tested. Walkability features were analyzed through a machine learning approach, utilizing the Random Forest Algorithm, with self-reported walking (meters walked per area unit). Results indicate that the most relevant walkability features were: Entropy (FI= 0.609), Integration at a 2000-meter radius (FI=0.136) and Residential Density (FI=0.060). These findings are of great implication to the operationalization of walkability in Brazilian cities, indicating that more traditional walkability models might not be ideal. Implications of these findings include informing local urban policies to adopt an evidence-based, contextually-tailored approach