VUCA bibliometric and content analysis

VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncer-tainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were pre-pared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productiv-ity. The content analysis analyzed 95 article s in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in deal-ing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small. © 2023 CSIC. Este es un artículo de acceso abierto distribuido bajo los términos de la licencia de uso y distribución Creative Commons Reconocimiento 4.0 Internacional (CC BY 4.0).

Analisis bibliometrico y de contenido sobre VUCA

analysis : VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncer-tainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were pre-pared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productiv-ity. The content analysis analyzed 95 article s in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in deal-ing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small.

Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv

Two thousand one hundred and ninety-eight research publications on COVID-19 vaccines in MedRxiv preprint repository during January 01, 2020 and December 31, 2021 were analyzed for topic modelling with unsupervised inference method. Latent Dirichlet Allocation (LDA) method was used to investigate the thematic structure of the preprints. It was observed that the published articles were related to either clinical trials or patient responses to vaccine or modelling for various applications such as infection transmission, vaccine allocation, vaccine hesitancy etc. © 2023, National Institute of Science Communication and Policy Research. All rights reserved.

Changes in public travel willingness in the post-COVID-19 era: Evidence from social network data

Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were-0.8197 and-0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.

Investigating Canadian Public Attitudes Toward COVID-19 Vaccine Mandates with a Nested Analysis Framework.

BACKGROUND: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this. METHODS: This study applied a "nested analysis" framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic. RESULTS: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups. CONCLUSION: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.

Impact analysis of COVID-19 pandemic on students' online exams based on topic model

To deal with the COVID-19 pandemic, schools at all levels insist on "classes suspended but learning continues"and actively implement online teaching. Different from the planned shift from offline to online education, COVID-19 caused online teaching to be highly sudden and emergent, producing different learning outcomes from offline teaching. Therefore, it is critical to analyze the epidemic's impact on students' learning outcomes. However, prior studies only focus on statistical data of the learning process, such as students' test scores or homework completion, rather than comments posted on social media. This paper explores the impact of COVID-19 on students' online exams by identifying potential topics during the final exam period. We first collect and preprocess a huge number of Weibo posts with natural language processing methods. Then, we explore related topics via LDA (Latent Dirichlet Allocation) model. Finally, the extensive experimental results demonstrate that our findings for the 16 topic groups have significant roles in exploring students' attitudes towards online exams and exam cheating. Furthermore, we found that the overall affective attitudes of users' postings tended to be negative. © 2022 IEEE.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Proposed information retrieval systems using LDA topic modeling for answer finding of COVID 19 pandemic: A brief survey of approaches and techniques

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA. © 2023 Author(s).

A Practical and Empirical Comparison of Three Topic Modeling Methods using a COVID-19 Corpus: LSA, LDA, and Top2Vec

This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed since the 1980s namely, latent semantic indexing or analysis (LSI/LSA), probabilistic LSI/LSA (pLSI/pLSA), naïve Bayes, the Author-Recipient-Topic (ART), Latent Dirichlet Allocation (LDA), Topic Over Time (TOT), Dynamic Topic Models (DTM), Word2Vec, Top2Vec and \variation and combination of these techniques. For researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm-Top2Vec- with two of the most conventional and frequently-used methodologies-LSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused s. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers. © 2023 IEEE Computer Society. All rights reserved.

Learning Analytics Overview: Academic Approach and Machine Learning Possibilities

In the Covid-19 era, traditional lecture-based teaching has been undergoing changes in learning design, learners' engagement, and technology integration. Online learning has become an integral part of education around the globe due to its flexibility in learning with respect to place and time. These online courses are available to larger audiences and enable students to have more freedom over the study process. However, freedom also means that instructors have less control to keep students making progress on the course. The flexibility of online courses is encouraging the students to enrol with a few clicks but most of these students are dropping out due to losing interest in the course contents within a few weeks. On the contrary, online learning produces large amounts of data that can be used to follow the learning process and give useful insights for both teachers and students. Learning analytics algorithms utilize these data to identify the factors and parameters that can explain learners' dropout rate, learning performance, as well as suggest possible actions to intrigue learners' active engagement. To conduct further research on the parameters affecting learners' participation in an online course, it is essential to find out the previous research works, best practices, and research trends of learning analytics. In the scope of this work, the authors formulated a search query to generate a pool of most relevant papers from the Scopus database and, hence, identified seven clusters which illustrate the landscape of the learning analytics domain. The authors also employed the Latent Dirichlet allocation (LDA) topic modeling algorithm which is a form of text data mining and statistical machine learning approach to compare the similarities with the clusters generated as a part of literature review. The authors further analyzed the papers in each cluster to identify the parameters which are significant to build a predictive model on learners' dropout rate. This work not only provides a baseline to conduct further research to find out the parameters affecting learners' retention rate but also introduces a systematic methodology to validate the findings of the literature review with a data-driven algorithmic approach. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

YouTube as a source of information: early coverage of the COVID-19 pandemic in the context of the construction industry

The COVID-19 pandemic has been the largest global crisis in recent decades. Apart from the countless deaths and health emergencies, the pandemic has disrupted several industries—including construction. For example, a significant number of construction projects have been interrupted, delayed, and even abandoned. In such emergencies, information gathering and dissemination are vital for effective crisis management. The role of social media platforms such as YouTube, Facebook, and Twitter, as information sources, in these contexts has received much attention. The purpose of this investigation was to evaluate if YouTube can serve as a useful source of information for the construction industry in emergency situations—such as during the early stages of the COVID-19 pandemic. The assessment was undertaken by distilling the coverage of the COVID-19 pandemic as it relates to the construction industry from the content shared via YouTube by leveraging Latent Dirichlet Allocation (LDA) topic modelling. The investigation also compared the timeline with which relevant content was shared via YouTube and peer-reviewed research articles to make relative assessments. The findings suggest that YouTube offered significant and relevant coverage across six topics that include health and safety challenges, ongoing construction operation updates, workforce-related challenges, industry operations-related guidelines and advocacy, and others. Moreover, compared to the coverage of the COVID-19 pandemic in the research literature, YouTube offered more comprehensive and timely coverage of the pandemic as it relates to the construction industry. Accordingly, industry stakeholders may leverage YouTube as a valuable and largely untapped resource to aid in combating similar emergency situations.

Understanding telemedicine service users' perceptions: A text mining analysis on social media discussion

Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study aims to understand telemedicine users' perceptions of their care services and identify the aspects of telemedicine that can be improved to enhance users' experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. © 2023 IEEE Computer Society. All rights reserved.

Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

Digital transformation in tourism: bibliometric literature review based on machine learning approach

Purpose: This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic. Design/methodology/approach: In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling. Findings: The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic;the largest ones are smart analytics, marketing strategies and sustainability. Originality/value: To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis. © 2023, Peter Madzík, Lukáš Falát, Lukáš Copuš and Marco Valeri.

Evolution Analysis of Network Public Opinion Theme Based on LDA Model

Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the 'COVID-19' event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability. © 2022 IEEE.

Media policy analysis and diplomatic interactions during COVID-19 between China and the United States in a comparative perspective

This is a comparative study of official diplomatic speeches regarding COVID-19, released by spokespersons for the Ministry of Foreign Affairs of the People's Republic of China (PRC) and documents from the United States Department of State China Archive. It explores how these speeches and documents reflect the US–China relations and the conduct of policies surrounding digital media in the two countries. We focus on the period from the start of the Wuhan lockdown, 20 January 2020, to the city's reopening on 8 April, and use several forms of content analysis to analyse the documents: Latent Dirichlet Allocation (LDA) topic modelling, sentiment network analysis and word clouds. We argue that the diplomatic relationship and political ideologies adopted by different political and media systems can have a major impact upon media policy implementation and guidance.

Computational vs. qualitative: analyzing different approaches in identifying networked frames during the Covid-19 crisis

Despite the increasing adaption of automated text analysis in communication studies, its strengths and weaknesses in framing analysis are so far unknown. Fewer efforts have been made to automatic detection of networked frames. Drawing on the recent developments in this field, we harness a comparative exploration, using Latent Dirichlet Allocation (LDA) and a human-driven qualitative coding process on three different samples. Samples were comprised of a dataset of 4,165,177 million tweets collected from Iranian Twittersphere during the Coronavirus crisis, from 21 January, 2020 to 29 April, 2020. Findings showed that while LDA is reliable in identifying the most prominent networked frames, it misses to detects less dominant frames. Our investigation also confirmed that LDA works better on larger datasets and lexical semantics. Finally, we argued that LDA could give us some primary intuitions, but qualitative interpretations are indispensable for understanding the deeper layers of meaning. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Topical Analysis of Depressive Mood Changes in Youth during the COVID-19 Pandemic

This paper investigates the mood changes of youth groups during the social closure control of the COVID-19 pan-demic and the primary causes of those changes, taking Chinese online video platforms as an example. We also compare the main concerns of various periods to provide feasible references and suggestions on psychological interventions for young people during the social closure control period. In this study, we identified mood changes during the COVID-19 pandemic with 31,213 comments on the news videos of the Bilibili video platform through four stages: data collection, data processing, LDA topic modeling, and mood identification. Through a comparative analysis, we investigated the topical features of young people's mood changes in three COVID-19 periods: pre-, mid-, and late-epidemic. As a result, we found that social isolation measures such as closure and homeschooling with long-term Internet use during the epidemic were more likely to cause depression in young people. © 2023 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.

Medical device companies crisis communication response to stakeholders during COVID-19: pre-crisis stage to new normal

Purpose: During COVID-19, this study aims to evaluate the crisis communication strategies (CCS) of Fortune 500 medical device businesses. These companies' CCS adoption is evaluated using data from the microblogging site Twitter. Design/methodology/approach: A total of 11,569 tweets were collected over the course of a year, from 31 December 2019 to 31 December 2020, and analysed using COVID-19's pre-crisis, crisis and new normal stages. The data acquired from Twitter is assessed using latent Dirichlet allocation-based topic modelling, valence aware dictionary for sentiment reasoning sentiment analysis and emotion recognition analysis and then further examined using fuzzy set qualitative comparative analysis to build a configurational model. The findings were compared to Cheng's (2018, 2020) integrated strategy toolkit for organisational CCS, which included 28 strategies. Findings: With positive sentiments across stages, companies chose "information providing”, "monitoring” and "good intentions” as the CCS. In the crisis and new normal stages of COVID, the emotion of "depression” was observed. Research limitations/implications: Researchers would be able to assess the CCS used through visual aids in the future by conducting a cross-industry examination using image analytics. Furthermore, by prolonging the study's duration, long-term changes in the CCS can be investigated. Practical implications: Companies should send real-time information to their stakeholders via social media during a pandemic, conveying good intentions and positive sentiments while remaining neutral. Originality/value: To the best of the authors' knowledge, this is one of the first studies to investigate the CCS patterns used by medical device businesses to communicate via social media during a pandemic. © 2023, Emerald Publishing Limited.