645 research outputs found
Sentiment analysis of COVID-19 cases in Greece using Twitter data.
Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics
for the last two decades, using different sources from social media to search engine records. More recently,
studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of
the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of
pandemics.
Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the
sentiment impact of COVID-19 cases in Greece in real time as related to cases.
Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year
and were examined towards two sentimental lexicons: one in English language translated into Greek (using the
Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track
i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness,
Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data.
Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of
COVID-19. The correlation coefficient (R2
) for the Vader lexicon is − 0.07454 related to cases and − 0.,70668 to
the tweets, while the other lexicon had 0.167387 and − 0.93095 respectively, all measured at significance level of
p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the
interest in COVID-19 declined after a certain time
Human behavior in the time of COVID-19: Learning from big data
Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities
Internet and Biometric Web Based Business Management Decision Support
Internet and Biometric Web Based Business Management Decision Support
MICROBE
MOOC material prepared under
IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials
Prepared by:
A. Kaklauskas, A. Banaitis, I. Ubarte
Vilnius Gediminas Technical University, Lithuania
Project No: 2020-1-LT01-KA203-07810
KEER2022
AvanttÃtol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202
31th International Conference on Information Modelling and Knowledge Bases
Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers
Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic
We study the causal effects of lockdown measures on uncertainty and sentiment
on Twitter. To this end, we exploit the quasi-experimental framework created by
the first COVID-19 lockdown in a high-income economy--the unexpected Italian
lockdown in February 2020. We measure changes in public sentiment using deep
learning and dictionary-based methods on the text of daily tweets geolocated
within and near the locked-down areas, before and after the treatment. We
classify tweets into four categories--economics, health, politics, and lockdown
policy--to examine how the policy affected emotions heterogeneously. Using a
staggered difference-in-differences approach, we show that the lockdown did not
have a significantly robust impact on economic uncertainty and sentiment.
However, the policy came at the price of higher uncertainty on health and
politics and more negative political sentiments. These results, which are
robust to a battery of robustness tests, show that lockdowns have relevant
non-health related implications
Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment
Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’.
Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy.
The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy.
The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools
Digital Interaction and Machine Intelligence
This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction
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