Quem consome Fake News? Uma análise comparativa do efeito da ideologia política Esquerda-Direita na crença, interpretação e divulgação

A criação e a disseminação de conteúdos falsos, como ações de instrumentalização política, sempre fez parte dos jogos de manipulação e das manobras políticas inerentes à sociedade humana. No entanto, atualmente, com um mundo cada vez mais digital, sem limitações geográficas que impeçam as pessoas de estar constantemente interligadas e conectadas, a desinformação pode ser disseminada de forma massiva e a uma velocidade sem precedentes, ameaçando os fundamentos basilares do jornalismo e as demais instituições democráticas. Numa época em que existe um profundo desprezo pela verdade e pela evidência científica, na qual a credibilidade e a confiança nas principais instituições públicas e políticas, estão em crise, a mentira, sob o disfarce de notícias legítimas, compete, no mesmo ambiente digital, com os meios de comunicação pela atenção de uma audiência cada vez mais fragmentada, polarizada e seletiva. Depois das eleições presidenciais americanas de 2016, a criação de fake news passou a ser uma arma política frequentemente utilizada por agentes estatais e/ou independentes, com o objetivo de obterem, essencialmente, ganhos políticos, desacreditando adversários e manipulando eleitores em períodos de campanha eleitoral. À semelhança de outros países ocidentais, também em Portugal a disseminação de fake news políticas através das redes sociais, procura destabilizar a vida pública e política da sociedade. Considerando este contexto, este trabalho visa analisar a suscetibilidade dos eleitores portugueses a fake news contemporâneas, politicamente enviesadas. A nossa investigação procura, deste modo, compreender a influência das identidades ideológicas e partidárias na crença e divulgação de fake news e notícias politicamente comprometedoras, em conformidade com diferentes estilos cognitivos de processar informação e diferentes práticas de consumir informação online. Interessa, desta forma, identificar possíveis assimetrias ideológicas (esquerda vs direita), cognitivas e partidárias no que diz respeito ao consumo e disseminação de fake news políticas, ao mesmo tempo que se realiza uma auscultação geral da vulnerabilidade do eleitorado português, quando exposto a este tipo de conteúdos. Desta forma, propondo a apresentação de um inquérito por questionário, foi desenvolvida uma metodologia capaz de conciliar diferentes instrumentos e procedimentos para identificar ideológica e partidariamente os participantes, avaliando a sua habilidade cognitiva e as suas práticas de consumir informação online. A par destes métodos, os participantes foram convidados a avaliar a credibilidade e a manifestar a intenção de partilhar um conjunto de títulos de fake news e notícias. Este estudo pretende ser um contributo relevante para a investigação nesta área, sobretudo em Portugal. Acreditamos que este trabalho possa ser enriquecedor para a literatura, nomeadamente no que diz respeito à conceção de uma estrutura validada para a medição da suscetibilidade à desinformação.The creation and dissemination of false content, such as political instrumentalization actions, has always been part of the manipulation games and political maneuvers inherent to human society. However, currently, with an increasingly digital world, without geographic limitations that prevent people from being constantly connected, disinformation can be disseminated massively and at breakneck speed, threatening the basic pillars of journalism and other democratic institutions. At a time when there is a profound contempt for truth and scientific evidence, in which the credibility and trust of the main public and political institutions is in crisis, the lie, under the guise of legitimate news, competes, in the same digital environment, with the media for the attention of an increasingly fragmented, polarized and selective audience. After the 2016 US presidential elections, the creation of fake news became a political weapon frequently used by the state and/or independent agents, with the objective of essentially obtaining political gains, discrediting political opponents and manipulating voters in electoral campaign periods. As in other western countries, in Portugal, the dissemination of political fake news, through digital social networks, seeks to destabilize society's public and political life. Considering this context, this work aims to analyze the susceptibility to contemporary, politically biased fake news. In this way, our research seeks to understand the influence of ideological and party identities on the belief and dissemination of fake news and politically compromising news, in accordance with different cognitive styles of processing information and different practices of consuming information online. It is therefore interesting to identify possible ideological (left vs. right), cognitive and partisan asymmetries with regard to the consumption and dissemination of political fake news, while at the same time conducting a general survey of the vulnerability of the Portuguese electorate, when exposed to this kind of content. Thus, proposing the presentation of an inquiry by questionnaire, we developed a methodology capable of reconciling different instruments and procedures to identify participants ideologically and in a party way, evaluating their cognitive ability and their practices when consuming information online. Alongside these methods, participants were invited to assess the credibility and express their intention to share a set of fake news and news headlines. This study intends to be a relevant contribution to research in this area, especially in Portugal. We believe that this work can be enriching for the literature, namely with regard to the design of a validated structure for measuring the susceptibility to disinformation

Intelligent Data Engineering and Automated Learning – IDEAL 2019 [electronic resource] : 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II /

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.Special Session on Fuzzy Systems and Intelligent Data Analysis -- Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences -- Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering -- Special Session on Machine Learning towards Smarter Multimodal Systems -- Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation -- New Interfaces for Classifying Performance Gestures in Music -- Special Session on Data Selection in Machine Learning -- Classifying Ransomware Using Machine Learning Algorithms -- Artificial Neural Networks in Mathematical Mini-Games for Automatic Students Learning Styles Identification: A First Approach -- The Use of Unified Activity Records to Predict Requests Made by Applications for External Services -- Fuzzy Clustering Approach to Data Selection for Computer Usage in Headache Disorders -- Multitemporal Aerial Image Registration Using Semantic Features -- Special Session on Machine Learning in Healthcare -- Brain Tumor Classification Using Principal Component Analysis and Kernel Support Vector Machine -- Modelling survival by machine learning methods in liver transplantation: application to the UNOS dataset -- Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images -- Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis -- A Clustering-Based Patient Grouper for Burn Care -- A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions -- Special Session on Machine Learning in Automatic Control -- A Method based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI -- Safe Deep Neural Network-driven Autonomous Vehicles Using Software Safety Cages -- Wave and viscous resistance estimation by NN -- Neural controller of UAVs with inertia variations -- Special Session on Finance and Data Mining -- A Metric Framework for quantifying Data Concentration -- Adaptive Machine Learning-Based Stock Prediction using Financial Time Series Technical Indicators -- Special Session on Knowledge Discovery from Data -- Exploiting Online Newspaper Articles Metadata for Profiling City Areas -- Modelling the Social Interactions in Ant Colony Optimization -- An Innovative Deep-Learning Algorithm for Supporting the Approximate Classication of Workloads in Big Data Environments -- Control-flow Business Process Summarization via Activity Contraction -- Classifying Flies Based on Reconstructed Audio Signals -- Studying the Evolution of the ‘Circular Economy’ Concept using Topic Modelling -- Mining Frequent Distributions in Time Series -- Time Series Display for Knowledge Discovery on Selective Laser Melting Machines -- Special Session on Machine Learning Algorithms for Hard Problems -- Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy -- SMOTE Algorithm Variations in Balancing Data Streams -- Multi-Class Text Complexity Evaluation via Deep Neural Networks -- Imbalance reduction techniques applied to ECG classification problem -- Machine Learning Methods for Fake News Classification -- A genetic-based ensemble learning applied to imbalanced data classification -- The feasibility of deep learning use for adversarial model extraction in the cybersecurity domain.This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI