Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination

The purpose of this study is to analyse COVID-19 related news published across different geographical places, in order to gain insights in reporting differences. The COVID-19 pandemic had a major outbreak in January 2020 and was followed by different preventive measures, lockdown, and finally by the process of vaccination. To date, more comprehensive analysis of news related to COVID-19 pandemic are missing, especially those which explain what aspects of this pandemic are being reported by newspapers inserted in different economies and belonging to different political alignments. Since LDA is often less coherent when there are news articles published across the world about an event and you look answers for specific queries. It is because of having semantically different content. To address this challenge, we performed pooling of news articles based on information retrieval using TF-IDF score in a data processing step and topic modeling using LDA with combination of 1 to 6 ngrams. We used VADER sentiment analyzer to analyze the differences in sentiments in news articles reported across different geographical places. The novelty of this study is to look at how COVID-19 pandemic was reported by the media, providing a comparison among countries in different political and economic contexts. Our findings suggest that the news reporting by newspapers with different political alignment support the reported content. Also, economic issues reported by newspapers depend on economy of the place where a newspaper resides

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Stance characterization and detection on social media

Stance detection refers to the task of identifying a viewpoint as either supporting or opposing a given topic. The current research on socio-political opinion mining on social media is still in its infancy. Most computational approaches in this field are limited to the independent use of textual elements of a user’s posts from social factors such as homophily and network structure. This thesis provides a thorough study of stance detection on social media and assesses various online signals to identify the stance and understand its association with the analysed topic. We explore the task of detecting stance on Twitter, which is a well-known social media platform where people often express stance implicitly or explicitly. First, we examine the relation between sentiment and stance and analyse the inter-play between sentiment polarity and expressed stance. For this purpose, we extend the current SemEval stance dataset by annotating tweets related to four new topics with sentiment and stance labels. Then, we evaluate the effectiveness of sentiment analysis methods on stance prediction using two stance datasets. Second, we examine the multi-modal representation of stance on social media by evaluating multiple stance detection models using textual content and online interactions. The finding of this chapter suggests that using social interactions along with other textual features can improve the stance detection model. Moreover, we show how an unconscious social interaction can reveal the stance. Next, we design an online framework to preserve users’ privacy concerning the implicitly inferred stance on social media. Thus, we evaluate the effectiveness of the two stance obfuscation methods and use different stance detection models to measure the overall performance of the proposed framework. Finally, we study the dynamics of polarized stance to understand the factors that influence online stance. Particularly, we extend the analysis of online stance signals and examine the interplay between stance and automated accounts (bots). Furthermore, we pose the problem of gauging the bots’ effect on polarized stance through a sole focus on the diffusion of bots on the online social network

Chatbot development to assist patients in health care services

Dissertação de mestrado integrado em Engenharia InformáticaDados de alta qualidade sobre tratamentos médicos e de informação técnica tornaram-se acessíveis, criando novas oportunidades de E-Saúde para a recuperação de um paciente. A implementação da aprendizagem automática nestas soluções provou ser essencial e eficaz na elaboração de aplicações para o utilizador para aliviar a sobrecarga do sector de saúde. Atualmente, muitas interações com os utentes são realizadas via telefonemas e mensagens de texto. Os agentes de conversação podem responder a estas questões, fomentando uma rápida interação com os pacientes. O objetivo fundamental desta dissertação é prestar apoio aos pacientes, fornecendo uma fonte de informação fidedigna que lhes permita instruir-se e esclarecer dúvidas sobre os procedimentos e repercussões dos seus problemas de saúde. Este propósito foi concretizado não apenas através de uma plataforma Web intuitiva e acessível, composta por perguntas frequentes, mas também integrando um agente de conversação inteligente para responder a questões. Para este fim, cientificamente, foi necessário conduzir a investigação, implementação e viabilidade dos agentes de conversação no domínio fechado para os cuidados de saúde. Constitui um importante contributo para a comunidade de desenvolvimento de chatbots, na qual se reúnem as últimas inovações e descobertas, bem os desafios actuais da aprendizagem automática, contribuindo para a consciencialização desta área.High-quality data on medical treatments and facility-level information has become accessible, creating new eHealth opportunities for the recuperation of a patient. Machine learning implementation in these solutions has been proven to be essential and effective in building user-centred applications to relieves the burden on the healthcare sector. Nowadays, many patient interactions are handled through healthcare services via phone calls and text message exchange. Conversation agents can provide answers to these queries, promoting fast patient interaction. The underlying aim of this dissertation is to assist patients by providing a reliable source of information to educate themselves and clarify any doubts about procedures and implications of their health issue. This purpose was achieved not only through an intuitive and accessible web platform, with frequently asked questions, but also by integrating an intelligent chatting agent to answer questions. To this end, scientifically, it was necessary to conduct the research, implementation and feasibility of closed-domain conversation agents for healthcare. It is a valuable input for the chatbot development community, which assembles the latest innovations and findings, as well as the current challenges of machine learning, contributing to the awareness of this field

Computer-Mediated Communication

This book is an anthology of present research trends in Computer-mediated Communications (CMC) from the point of view of different application scenarios. Four different scenarios are considered: telecommunication networks, smart health, education, and human-computer interaction. The possibilities of interaction introduced by CMC provide a powerful environment for collaborative human-to-human, computer-mediated interaction across the globe

The Proceedings of the 23rd Annual International Conference on Digital Government Research (DGO2022) Intelligent Technologies, Governments and Citizens June 15-17, 2022

The 23rd Annual International Conference on Digital Government Research theme is “Intelligent Technologies, Governments and Citizens”. Data and computational algorithms make systems smarter, but should result in smarter government and citizens. Intelligence and smartness affect all kinds of public values - such as fairness, inclusion, equity, transparency, privacy, security, trust, etc., and is not well-understood. These technologies provide immense opportunities and should be used in the light of public values. Society and technology co-evolve and we are looking for new ways to balance between them. Specifically, the conference aims to advance research and practice in this field. The keynotes, presentations, posters and workshops show that the conference theme is very well-chosen and more actual than ever. The challenges posed by new technology have underscored the need to grasp the potential. Digital government brings into focus the realization of public values to improve our society at all levels of government. The conference again shows the importance of the digital government society, which brings together scholars in this field. Dg.o 2022 is fully online and enables to connect to scholars and practitioners around the globe and facilitate global conversations and exchanges via the use of digital technologies. This conference is primarily a live conference for full engagement, keynotes, presentations of research papers, workshops, panels and posters and provides engaging exchange throughout the entire duration of the conference

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods