Local Newspaper Coverage of the COVID-19 Pandemic in Two Major Texas Cities: A Visual Comparison of Houston and El Paso

In the US, the absence of a coordinated national response to the COVID-19 pandemic left decision-making to state and local leaders. In Texas, debate over how best to decrease the virus' spread highlighted political tensions between the Republican state leadership and the predominantly Democratic county- and city-leaders. We analyze the daily newspapers of two major cities, Houston and El Paso, to understand similarities and differences in local pandemic-related concerns. We focus specifically on three periods: the days immediately following the first case of COVID-19 in Texas in March 2020, the days surrounding the peak of the first major spike in July 2020, and the days surrounding the second, more deadly spike in January 2021. We trace the progression of the pandemic in Houston and El Paso, analyzing the prevalent newspaper themes and illustrating regional differences through word clouds, which provide a visual analysis of the COVID-19 related coverage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The impact of the learning shift during COVID-19 on students using natural language processing

On 9th March 2020, Saudi Arabia has proclaimed the temporary transition to remote learning due to COVID-19. We underline students' perspectives on this abrupt transformation. We generate a word cloud based on the students' responses concerning the rapid transition. The feedback based on emotions was classified and a word cloud for each emotion was generated. For better decision making and improved strategies, we highlight the major problems and benefits of remote learning and provide some recommendations. Students have experienced a variety of hurdles, including the lack of an adequate study environment and technical difficulties, particularly when taking exams. Many were under psychological pressure. Others saw an increase in cheating. Some struggled to work with their peers on group projects, some sought tutoring, and others faced financial difficulties. Online practical sessions were found to be unsuitable for some disciplines. The flexibility of learning and saving money and time were the main advantages of remote learning.

Analyzing Student Evaluations of Teaching in a Completely Online Environment

Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students' evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and "good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers' overall performance and the quality of their teaching.

Technology enabled communication during COVID 19: analysis of tweets from top ten Indian IT companies using NVIVO.

The corona virus (COVID-19) pandemic has impacted industries across the globe. Lockdown was imposed to curb the spread of the deadly virus. This resulted in closure of the factories and manufacturing units. Few sectors switched to work from home (WFH) for the first time. The present study aims to understand and analyze the way in which Information Technology (IT) sector communicated on Twitter during the pandemic. The top ten IT companies in India were selected on the basis of net sales. Qualitative data analysis was employed to extract tweets, understand and analyze them. Tweets were extracted from the official Twitter handles of these top ten IT companies using N-Capture extension tool of NVivo 12 software from April 1, 2020 to April 30, 2021. To get insights out of collected data, Word Cloud, TreeMap and Sentiment Analysis of tweets were carried out using NVivo 12 software. The research found that IT companies focussed on digital transformation, business development, customer satisfaction and enriching customer experience, new product development for healthcare and insurance and organizational resilience. They also focussed on effective communication through Twitter in times of crisis. Most of the companies tweeted moderately positive. Very small numbers of tweets were found to be very negative.

Strategic Airline Service Review in the Time of the COVID-19 Crisis

This study extracted airline data from several online source to examine operational and service strategy of the airline industry during the COVID-19 pandemic. The results have suggested that airlines were losing market shares in this pandemic situation except for those with high assets. In addition, this study utilizes text analytics techniques to provide insight into service characteristics that distinguish positive from negative reviews. The results suggest that satisfied travelers are demanding services with high empathy and responsiveness, while negative reviewers frequently complain about insufficient operational aspects such as ground operations, mishandled baggage, system glitches, and staff management on handling cancellation. Copyright © 2022, IGI Global.

Exploration of Twitter Data Linked to Psychological Behaviors During COVID 19 Pandemic-Visualization Approach

In the era of Covid 19 pandemic, governments have imposed nationwide lockdowns which make a huge change to people daily routines. This last affect indirectly on the well-being of people’s mental health, especially the vulnerable population. And due to social media, many conversations about these phenomena occur online, especially those related to people’s emotions. Then the field of sentiment analysis is requested. In this paper, we aimed to extract correlations within this epidemic and its psychologic effects. In fact, our goal is to extract features that may improve sentiment analysis accuracy which is a crucial step to fulfill the main objective of our research: developing an intelligent recommendation system that will benefit persons, through a positive accompaniment and the early alert, in case of complex situations as mental illness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques

It’s been around two years from the outbreak of the coronavirus, thus labeled as Covid-19, and there has been an explosion of literature being published by research scholars related to work done on Covid-19. Covid-19 as a keyword has been mentioned in the titles of most of these papers. It was thought to analyse the number of papers and the titles of papers which include Covid-19 in the title of the research papers. The various combinations of other words like, prefixes, suffixes, N-gram combinations with the keyword Covid- 19 in the titles of these papers were also analysed. The research publication repositories analysed were: IEEE Explore, ACM Digital Library, Semantic Scholar, Google Scholar, Cornel University etc. The domains of research publication title analysis were restricted to computer science/computer engineering related papers. As the term labeling the corona virus outbreak as Covid-19 was labeled in 2020, the timeline of title analysis was restricted from 2019 till December 2021. The term Covid-19 is also one of the most searched terms in most of these research repositories as is evident from the search suggestions offered by them. Considering the usefulness of Bag of Words and N Gram algorithm in analytics and data visualization, a methodology is proposed and implemented based on bag of words algorithm to do prefix and suffix words analysis. This methodology is working correctly to state different prefix and suffix words used by various researchers to demonstrate significance of their titles. Methodology based on N Gram analysis is found effective to find topic on which most of the researchers have done work. Word Clouds are generated to demonstrate different buzz words used by researchers in their respective paper titles. These are useful for providing visualization of the data if it is in big size. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques.

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Current Trend of Virology in the Post COVID-19 Era with Related Topic-Based Tweet Analysis

In light of current trends in virology, we performed social media analysis of 13 main topics in the area of virology and ranked these topics with metrics such as users, posts, engagement, and influence. These metrics were monitored against the 13 keywords on Twitter for the same period (i.e., from 27 November to 6 December 2021) for benchmarking purposes. The 13 main topics were "virological Science", " preventive vaccines", "therapeutic vaccines", "viral pathogenesis", "viral immunology", "antiviral strategies", "virus structure", "virus expression", "viral resistance", "emerging viruses", "interspecies transmission", "viruses and cancer" and " viral diseases". "viral diseases" recorded the highest number of users (i.e., 905 users) and the highest number of post (i.e., about 1K posts). The second-highest number of posts were monitored to be on "therapeutic vaccines" with 729 posts from 691 users. In terms of engagement, "viral diseases" (3.4 K) were found to be on the top followed by "viruses and cancer" (3.1K). Lastly, in terms of influence, "viral diseases" recorded 9.0 million influences followed by 6.6 million influences on "emerging viruses". In summary, "viral diseases" was found to be the most engaging and influential topic highest with the highest number of posts from most of the tweet users. In relation to trending hashtags in virology, #COVID19 recorded the highest number of hashtags, followed by # omicron, #sarscov2, #publichealth, #omicronvarient, #wuhan, #originofcovid, #fauci and #epidemiology. Word clouds showing the main area of discussion were also generated for these 13 main topics.

Hydrometeorological disasters during COVID-19: Insights from topic modeling of global aid reports.

Since the beginning of the COVID-19 pandemic, the world has experienced numerous hydrometeorological disasters along with it. The pandemic has made disaster relief work more challenging for humanitarian organizations and governments. This study aims to provide an overview of the topics/issues of concern in the countries while responding to hydrometeorological extreme events (e.g., floods and cyclones) during the pandemic. Latent Dirichlet Allocation (LDA), a computational topic modeling technique, is employed to reduce the numerous (i.e., 1771) humanitarian reports/news to key terms and meaningful topics for 24 countries. Several insights are derived from the LDA results. It is identified that countries have suffered multiple crises (such as locust attacks, epidemics and conflicts) during the pandemic. Maintaining social distancing while disaster evacuation and circumventing the lockdown for relief work have been difficult. Children are an important topic for most countries; however, other vulnerable groups such as women and the disabled also need to be focused upon. Hygiene is not a highly weighted topic, which is of concern during a pandemic that mandates good sanitation to control it effectively. However, health is of great importance for almost all countries. The novelty of the paper lies in its interdisciplinary approach (usage of a computational technique in disaster management studies) and the timely examination of disaster management experiences during the ongoing pandemic. The insights presented in the study may be helpful for researchers and policy-makers to initiate further bottom-up work to address the challenges in responding to hydrometeorological disasters during a pandemic.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were “family”, “anxiety”, “house”, and “life”. Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Twitter Sentiment Analysis: Caribbean Prime Ministers Response to COVID-19 Pandemic

In today's world, millions of people use social networking and microblogging sites every day to share their views, opinions, and emotions in their daily lives. These sites can become an invaluable source for data mining and can be used effectively to evaluate people's opinion on a product, an entity or perhaps topics of interest. Sentiment Analysis, as it is called, allows us to determine whether the opinions, mood, views, or attitude in a text is either 'positive', 'negative', or 'neutral'. The focus of this study was to analyze the tweets of the top ten English-speaking Caribbean Prime Ministers on Twitter to determine how effective they were in reducing the spread of the COVID-19 outbreak in their territories. The research results provided clear evidence that the negative sentiment towards the virus by the Caribbean leaders was a contributing factor in reducing the number of cases and deaths during the first five months of COVID-19 in the region. The results also found that a correlation exists between the prime ministers' social network and their effectiveness in managing the virus. In addition, the words expressed by the prime ministers in reference to COVID-19 were clear and practical therefore making it easier for the prime ministers to implement strict measures to control the spread of the virus in the region. © 2021 IEEE.

Does Online Education Ensure Effective Teaching and Evaluation of Geography? A Romanian Perspective of 5th Graders

Online education developed greatly during the Covid 19 Pandemic. Although there were online learning and teaching resources before 2020, they were not sufficiently tested or used. In modern geography, students must develop their skills, knowledge, be motivated and involved in geographic inquiry. Our objectives are related to the research question of this study, namely how students perceive this new form of evaluation, online evaluation, and whether they have certain preferences related to the tools used in online assessment (Google Forms and Wordwall). Data on students' perceptions regarding these online assessment tools were collected through an online questionnaire on a sample of 85 fifth graders. The analysis methods were word cloud analysis and multivariate statistical analysis. The results obtained showed that students are open to online assessment through new methods. Moreover, this type of assessment offers them a simpler alternative to learn, with them better understanding or easily remembering the taught lesson. The appearance of the two user-friendly interface platforms or the easy to use mode is an important variable perceived by students, as they can induce in students the joy of participating in an online competition. There are also negative aspects reported by them, especially related to concerns regarding the internet connection or to time given being too short. The usefulness of these tools is not to be neglected at all, given that the target group has been continuing online education for more than a year and the teaching-learning process must adapt to the current context. © 2021 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.

Trump's COVID-19 tweets and Dr. Fauci's emails.

The paper features an analysis of former President Trump's early tweets on COVID-19 in the context of Dr. Fauci's recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot's power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act.

Sentiment Analysis of Tweets during COVID-19 Pandemic Using BLSTM

social media is the new way for people to express and share their thoughts and it plays a huge role in spreading the anxiety widely during the pandemics and Twitter is one of the social media channels with high number of users and daily tweets. Clarifying and understanding what people thinks and their shared opinions during such hard times can reduce the burden on the health systems and entities and redirects the concerned entities by highlighting the areas to spread the awareness in. The aim of this study is to analyze and assess the sentiment of the Tweets shared during the COVID-19 pandemic and testing the Bidirectional Long Term Short Memory (BLTSM) of Recurrent Neural Network (RNN) in predicting the sentiment class which are Negative, Positive and Neutral. The results show slight difference between the positive and negative tweets which needs an attention to spread the awareness and hence, reduce the negativity. Furthermore, the BLSTM predicted the sentiment classes (Negative, Positive and Neutral) and obtained 86.15% accuracy rate. The high accuracy concludes that BLSTM can adapt and predict the sentiment of a text. © 2021 IEEE.

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models.

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88-87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

Influence of the pandemic dissemination of COVID-19 on facial rejuvenation: A survey of Twitter.

BACKGROUND: With the pandemic dissemination of COVID-19, attitude and sentiment surrounding facial rejuvenation have evolved rapidly. AIMS: The purpose of this study was to understanding the impact of pandemic on the attitude of people toward facial skin rejuvenation. METHODS: Twitter data related to facial rejuvenation were collected from January 1, 2020, to April 30, 2020. Sentiment analysis, frequency analysis, and word cloud were performed to analyze the data. Statistical analysis included two-tailed t tests and chi-square tests. RESULTS: In the post-declaration, the number of tweets about facial rejuvenation increased significantly, and the search volume in Google Trends decreased. Negative public emotions increased, but positive emotions still dominate. The words frequency of "discounts" and "purchase" decreased. The dominant words in word cloud were "Botox," "facelift," "hyaluronic," and "skin." CONCLUSION: The public has a positive attitude toward facial rejuvenation during the pandemic. In particular, minimally invasive procedures dominate the mainstream, such as "Botox," "Hyaluronic acid," and "PRP." The practitioners could understand the change of the public interest in facial rejuvenation in time and decide what to focus on.