Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis of Covid-19

Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.

Analyzing the Evolution of Disinformation Content on Facebook - a Pilot Study

Disinformation spread on social media generates a truly massive amount of content on a daily basis, much of it not quite duplicated but repetitive and related. In this paper, we present an approach for clustering social media posts based on topic modeling in order to identify and formalize an underlying structure in all the noise. This would be of great benefit for tracking evolving trends, analyzing large-scale campaigns, and focusing efforts on debunking or community outreach. The steps we took in particular include harvesting through CrowdTangle huge collection of Facebook posts explicitly identified as containing disinformation by debunking experts, following those links back to the people, pages and groups where they were shared then collecting all posts shared on those channels over an extended period of time. This generated a very large textual dataset which was used in the topic modeling experiments attempting to identify the larger trends in the available data. Finally, the results were transformed and collected in a Knowledge Graph for further study and analysis. Our main goal is to investigate different trends and common patterns in disinformation campaigns, and whether there exist some correlations between some of them. For instance, for some of the most recent social media posts related to COVID-19 and political situation in Ukraine. © 2022 Copyright for this paper by its authors.

Learning the Protein Language Model of SARS-CoV-2 Spike Proteins

Ahstract-SARS-CoV-2 virus has long been evolving posing an increased risk in terms of infectivity and transmissibility which causes greater impact in communities worldwide. With the surge of collected SARS-CoV-2 sequences, studies found out that most of the emerging variants are linked to increased mutations in the spike (S) protein as observed in Alpha, Beta, Gamma, and Delta variants. Multiple approaches on genomic surveillance have been performed to monitor the mutational status and spread of the virus however most are heavily dependent on labels attributed to these sequences. Hence, this study features a system that has the capability to learn the protein language model of SARS-CoV-2 spike proteins, based on a bidirectional long-short term memory (BiLSTM) recurrent neural network, using sequence data alone. Upon obtaining the sequence embedding from the model, observed clusters are generated using the Leiden clustering algorithm and is visualized to monitor similarities between variants in terms of grammatical probability and semantic change. Additionally, the system measures the validity of a user-generated next-generation sequence capturing potential sequence mutations indicative of viral escape, particularly mutations by substitutions. Further studies on methods uncovering semantic rules that govern spike proteins are recommended to learn more about other viral characteristics conclusive of the future of the COVID-19 pandemic. © 2023 IEEE.

A Novel Machine Learning Classification Model to Analyze the COVID-19 Pandemic's Impact on Academics: Sentiment Analysis Approach

The COVID-19 Pandemic is considered as the worst situation for human beings;it affected people's lives worldwide. Due to this pandemic, the respective government authority announced the lockdown to break the coronavirus chain. The lockdown impacted people's mental health, leading to many psychological issues as well as hampered students' academics. In this chapter we have studied the impacts on students' academics due to lockdown effect. The data has been collected via a google form questionnaire circulated to various educational institutes. Further, we have developed a novel machine learning classifier model called Naïve Bayes-Support Vector Machine for analyzing the data, which utilizes the properties of both classifiers by using a deep learning framework. We have used natural language processing (TextBlob, Stanza and Vader) libraries to label the dataset and applied in the proposed NBSVM method and other machine learning models and classified the sentiments into two categories (Positive vs Negative). We also applied the natural language processing libraries used a topic-modelling technique called Latent Dirichlet Allocation to know the essential topics words of both classes from students' feedback data. The study revealed 83% and 86% accuracy for unigram and bigram, respectively, whereas the precision was 79% and recall 81%. According to NLP libraries' result, approximately 71% of the feedback's sentiment is negative, and only 16% of feedbacks are positive. The proposed model shown that (Naïve Bayes-Support Vector Machine) outperforms the other variants of the Naïve Bayes and support vector machine. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Distilling Business Value from COVID - 19 Public Media Dataset with Machine Learning and Natural Language Processing

While manual analysis of news coverage is difficult and time consuming, methods in natural language processing can be used to uncover otherwise hidden semantics. This work analyses more than 370,000 news articles to explore connections and trends in business decisions and their financial impact during the COVID-19 pandemic. Topic modelling, sentiment analysis and named entity recognition methods are used to identify connections between the articles and the financial performance of selected companies or industries. This report sets out the results of the individual natural language processing methods and the resulting analysis with financial data. Interesting contrasting topics in the media can be filtered out that are associated with the companies with the highest or lowest positive sentiment. This information could be useful to companies to gain an understanding of topics that are currently treated favourably or unfavourably by the media and hence assist with communication strategies and competitive intelligence. © 2022 IEEE.

Media Representation of China in COVID-19 Reports: Text Mining of the Language of New York Times

Textual mining, an application of natural language processing and analytical methods, effectively turns text into data, making machine analytics possible, especially in the field of textual framing and sentiment analysis, traditionally classified manually by researchers which unavoidably involves tremendous manpower and time. This study examines the themes and sentiments of news coverage of China against the backdrop of Covid-19 in the New York Times (NYT) ranging from January 2020 to January 2021 by employing the LDA topic modeling textbfand (Natural Language Toolkit) Vader SentimentAnalysertextbf. The result of a combination of quantitative and qualitative analysis reveals the foci and attitudes of NYT and highlights the selection, emphasis and exclusion practices in this Western media. The study thus broadens the scope of existing content analyses of the image of China and contributes to the exploratory application of text mining techniques in media and linguistic studies. © 2022 IEEE.

Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evolving Perceptions of Mental Health on Social Media and their Medical Impacts

Mental health is an ever-growing issue of concern, especially in light of the COVID pandemic. In this context, we study big data from social media over a 7-year time span to gauge evolving perceptions of mental health, and discuss our research findings, potentially useful for decision support in healthcare. We deploy topic modeling and sentiment analysis to estimate public perceptions of mental health issues, focusing on Twitter as the social media site. We claim that it is important to consider polarity as well as subjectivity in sentiment analysis to comprehend two different aspects of sentiment, i.e. orientation in the emotion, and extent of fact vs. opinion. We assert that ranking via topic modeling is beneficial to fathom the relative importance of issues over the years. We harness tools/techniques from natural language processing and data mining to discover knowledge from big data on social media, related to mental health. Some of our findings reveal that the sentiment around mental health has remained positive overall, but has decreased since the beginning of the COVID pandemic. Major events, such as elections and the pandemic, greatly impact the conversation surrounding mental health. Some topics have remained consistent throughout the years. In other topics, the tone of the public discussions has shifted. The outcomes of our study would be useful to a variety of professionals, ranging from data scientists to epidemiologists and psychologists. This work impacts big healthcare data in general. © 2022 IEEE.

Media Coverage and Public Perception of Distance Learning During the COVID-19 Pandemic: A Topic Modeling Approach Based on BERTopic

The current study proposes a topic modeling approach for analyzing press coverage and public perception of distance learning during the COVID-19 pandemic. We evaluate the applicability of a novel approach for neural topic modeling based on transformer-based language models. Our methodology is tested empirically on a large sample of news and discussions in various social and news media platforms to derive valuable insights on press coverage and public perception of COVID-19 impact on the education system in Bulgaria. The study outlines key advantages of using BERTopic in analyzing big data. Our work contributes to the body of literature devoted on value creation through big data and text analytics utilization in the public sector. © 2022 IEEE.

Health Misinformation in the Covid-19 Era - Detecting Misinformation on Bi-lingual Corpora using Lexical Features

Social media use spiked amid the COVID-19 pandemic, resulting in an increase in fake news proliferation, especially health misinformation. Many misinformation detection studies have primarily focused on English texts, and of these, very few have examined linguistic features (syntactic, lexical, and semantic). Lexical features such as number of upper-case letters have been shown to improve misinformation detection in English and non-English texts, however, use of lexical features is still in its infancy, and thus warrants further investigation. Therefore, a novel lexical-based health misinformation detection model is proposed using machine learning techniques, specifically focusing on two languages, namely, English, and standard Malay. A new dataset containing fake and real news were developed from a fact- checking portal and local media, targeting news related to COVID-19. Common natural language processing tasks including filtering, tokenization, stemming etc. and lexical feature extraction were administered prior to data modelling. Evaluation on a dataset containing 1060 fake and real news each show Random Forest to yield the best performance with 99.6% for F-measure and accuracy of 96%, followed closely by Support Vector Machine. A similar observation was noted for the Malay corpus. Improved health misinformation detection was observed when linguistic features were included as part of the model, hence implying that the features can be successfully used in detecting fake news. © 2022 IEEE.

A Methodology for DSML-Assisted Participatory Agent-Based Enterprise Modelling

Participatory agent-based modelling (ABM) can help bring the benefits of simulation to domain users by actively involving stakeholders in the development process. Collaboration in enterprise modelling can improve the model developer's understanding of the domain and therefore improve the effectiveness of domain analysis. Where many agent-oriented methodologies focus on the development of one-off models, domain-specific modelling languages (DSML) can improve the re-use of concepts identified in domain analysis across multiple case studies and expose modelling concepts in domain-appropriate terms, increasing model accessibility. To realise the benefits of DSMLs we need to understand how DSML development can be incorporated into typical agent-based modelling. In this paper we discuss existing methodologies for ABM development and DSML development, and we discuss the benefits merging the two can bring. We present a methodology for DSML-assisted participatory agent-based modelling, and support the methodology with a case study—a modelling exercise conducted in collaboration with a hospital emergency department on the topic of infection control for COVID-19 and Influenza. © 2022, IFIP International Federation for Information Processing.

Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

Turkce Radyoloji Raporlarinin Dogal Dil Islenmesi

Today, it is widely used on DDI. is an important problem. machine with Natural Language Processing understand human language and facilitate human life is targeted. Word preprocessing, information extraction, machine translation, text summarization and classification studies DDI are some of the application areas. Education, business and social Studies on DDI in almost every sector such as media available. Declaring a pandemic due to the Covid19 Virus in the world DDI became important in the health sector after the won. Studies on Turkish texts scarcity and the need for DDI applications in the field of health makes the work important. Turkish Radiology A dataset was created from the reports. Data NLTK, A set of rules specified by the mainspring and regular expressions. preprocessed and normalized. later Language modeling was carried out with Word2Vec. © 2022 IEEE.

How Do Engineering Students Characterize Their Educational Experience on a Popular Social Media Platform Before and During the Covid-19 Pandemic?

Public social media platforms can supplement our understanding of student perceptions of engineering teaching. Looking to social media can help us build a picture of what steps we can take to improve the learning experience. It has the potential to provide meaningful information without requiring more data collection from students. This is particularly salient in times of crisis when contact with students may be inconsistent and when data such as survey results may be more challenging to obtain. In this study, we analyzed social media data from Reddit towards developing an understanding of engineering students' attitudes and focus areas around their educational experience before and during the Covid-19 pandemic. Students' attitudes were mainly evaluated by sentiment analysis and students' focuses were explored through topic modeling techniques. Both sentiment analysis and topic modeling are a form of natural language processing. Sentiment analysis is a tool to study the feelings expressed in text while topic modeling allows us to look for groups of related phrasings and to obtain a sense of the topics being discussed. Both are readily available through open-source Python packages. Based on the sentiment analysis, findings were categorized as positive, negative, and neutral. Within these three areas, we used topic modeling to categorize and explore the different emphasis areas brought up by students (e.g., extracurricular activities, school assignments). We present the results of the modeling using a topic visualization and interpretation tool. Although this work illustrates computational methods for analyzing social media data, these tools are seen pragmatically as a means to an end and not the sole purpose of our inquiry. Social media analyses have limitations and ethical considerations, and this work is not meant to supersede other forms of evaluation. Rather, our study explores the use of social media as a potential complementary source of data for practitioners. Our work has implications for educators and institutions looking to develop low-impact ways to evaluate educational programming in times of crisis and beyond. We hope that by presenting this work to other researchers and practitioners in engineering education, we will engage in mutually beneficial conversations around the pros and cons of using social media data and its potential applications. © American Society for Engineering Education, 2022

A Model Driven Approach for Unifying User Interfaces Development

In this paper, we dealt with the rapid development of client web applications (frontend) in a context where development frameworks are legion. In effect with the digital transformation due to the COVID-19 pandemic we are witnessing an ever-increasing demand of the application development in a relatively short time. To this is added the lack of skilled developers on constantly evolving technologies. We therefore offer a low-code platform for the automatic generation of client web applications, regardless of the platform or framework chosen. First, we defined an interface design methodology based on a portal. We then implemented our model driven architecture which consisted of defining a modeling and templating language, centered on user data, flexible enough to not only be used in various fields but also be easily used by a citizen developer. © 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.

A Large-Scale Analysis of COVID-19 Twitter Dataset in a New Phase of the Pandemic

Social media have been awash with news and discussions of the COVID-19 pandemic. It is phenomenal to observe that social media has been the focal venue for people to express their reactions, opinions, and interpretations of the pandemic, given the presence of mixed sources of real information and misinformation. Thus, it is essential to conduct professional assessments of the public views and their evolving nature. Our study aims to extract and assess insights into the reflections of sentiments and topics of the public on Twitter and their dynamics along the timeline of the Delta variant. It highlights the extraordinary influence Twitter, or similar major social media, would have on people to comprehend and decide how to cope with the pandemic. We present findings of extracted sentiments and topics from a large-scale dataset of COVID-related tweets collected for the recent phase of the Delta variant of the pandemic (July-September 2021). We utilized a variety of machine learning algorithms for topic modeling and testing the accuracy of sentiment analysis. Our study shows the dramatic dominance of a positive and objective sentiment rather than a negative and subjective sentiment as well as the shift of prevalent topics during the period of study. The findings indicate the importance of conveying real, rational, and accurate information instead of misinformation on social media to foster the public's awareness and preparedness for a major public emergency incident such as the pandemic. © 2022 IEEE.

Adapting and Evaluating the Story-Card-Method

Domain modelling languages (DMLs) grow and change over time. These languages are artefacts that are developed within communities via multiple participants. Methods, associated with the emerging DMLs, also need to be supported and need adaptation, informed by practice. This study refers to a DML called DEMO (Design and Engineering Methodology for Organizations), of which the language specification evolved from the DEMO Specification Language (DEMOSL) version 3 to version 4. We adapt a method, called the story-card-method (SCM), to accommodate DEMOSL 4. Also, the previous DEMOSL 3-based SCM implied physical interaction between participants, using sticky notes to create a shared understanding, whereas the adapted SCM has to facilitate a digital way-of-collaboration due to COVID-19 restrictions. We re-visit participant feedback from the initial version of the SCM and demonstrate how we applied design science research to design an adapted SCM as the main contribution of this article. In addition, we evaluate whether the adapted SCM is useful in providing ample guidance in compiling a Coordination Structure Diagram (CSD) in a collaborative way, and we evaluate the quality of the CSDs. Finally, we demonstrate how the CSD can be used within a low-code-development ecosystem to structure user stories. © 2022, Springer Nature Switzerland AG.

A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of the aforementioned techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin. © 2022 IEEE.

Extracting Major Topics of COVID-19 Related Tweets

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period;however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the "reopening", "death cases", "telecommuting", "protests", "anger expression", "masking", "medication", "social distance", "second wave", and "peak of the disease"titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time. © 2021 IEEE.

Topical Sentiment Classification to Unmask the Concerns of General Public during COVID-19 Pandemic using Indian Tweets

One of the vibrant social media platforms is which has more than half a million uses across the globe. It has become a popular means for dissemination of the news, to discuss on world events. It is also medium to converse about health centric information with updates given by the concerned officials and general public health-related information, during an abnormal situation like COVID-19 pandemics. As the dimension of data and the linguistics of data been discussed is diverse in nature, it is a challenging task to identify only the content that is interesting and useful. Few studies have been done exploring the regional languages than other English. In this work, we explored huge number of tweets on post-lock down during Covid-19 pandemic by analyzing the sentiments expressed on the tweets and topic identification. To do the same we have employed English 2,126,421 and 76,265 Tamil tweets for analyzing and discussing the usefulness of sentiment analysis and topic modeling in both of the languages. Seven subjects were that are ranked on the analysis of content discussed from India, in Twitter during the four months from May 2020 and August 2020. Tamil tweets are investigated to understand the sentiments of people during anamount of time and the association to the media information published, and the assessment of the psychological behavior of human in India. It is always significant to understand the human opinions, information communication and building an agreement including social media in different regions of the nation. © 2021 IEEE.