ABSTRACT
Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users' perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level. Copyright © The Author(s) 2023.
ABSTRACT
This paper introduces a corpus for Arabic newspapers during COVID-19: AraNPCC. The AraNPCC corpus covers 2019 until 2021 via automatically-collected data from 12 Arab countries. It comprises more than 2 billion words and 7.2 million texts alongside their metadata. AraNPCC can be used for several natural language processing tasks, such as updating available Arabic language models or corpus linguistics tasks, including language change over time. We utilized the corpus in two case studies. In the first case study, we investigate the correlation between the number of officially reported infected cases and the collective word frequency of "COVID” and "Corona.” The data shows a positive correlation that varies among Arab countries. For the second case study, we extract and compare the top 50 keywords in 2020 and 2021 to study the impact of the COVID-19 pandemic on two Arab countries, namely Algeria and Saudi Arabia. For 2020, the data shows that the two countries' newspapers strongly interacted with the pandemic, emphasizing its spread and dangerousness, and in 2021 the data suggests that the two countries coped with the pandemic. © European Language Resources Association (ELRA).
ABSTRACT
In situations of crisis, governments must acknowledge that communication is a major weapon in their armoury, and can be used to convince the public to accept sometimes stringent measures, while preventing a worsening of the situation by curbing any spread of panic. Theoretically, during a pandemic, fear can be contained at reasonable levels by governments counterbalancing uncertainty with information. However, there is no empirical evidence on how the flow of information during a crisis can influence emotional states among the population. In this process, social media appears to be a valuable tool for governments to observe emotional response in a population. In the light of this and within the context of the Italian government's social media campaign #iorestoacasa ('I'm staying at home') launched during the Covid-19 crisis, the current study utilises text analytics to explore the relationship between government and press communication, and the level of fear expressed by citizens through more than 200 thousand #iorestoacasa tweets. The results highlight how the content of the messages evolved in the early part of the outbreak and during the social media campaign. They suggest that in Italy the discussion regarding the efforts made by the European Council to find common solutions for dealing with the emergency has prompted a positive influence on public mood. Conversely, messages about people's individual vulnerability and the associated sense of an external locus of control correlated positively with levels of fear. This study opens new ways to support government communication during a crisis by monitoring public emotional response through social media.
ABSTRACT
Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.
ABSTRACT
The state of Iowa conducted an initial business survey in March 2020 as the novel coronavirus disease 2019 (COVID-19) broke out across the United States. The survey data have been used for decision and policy making at the state level. Relief incentive packages were provided via the Iowa Economic Development Authority (IEDA) to Iowa-based companies to support their operations. A team of policy makers, faculty, and industry professionals was formed to conduct text analyses, analyze the survey responses, validate insights, and ensure that the appropriate policies were enacted. The analysis yielded a reproducible process using the statistical software R to quickly analyze large volumes of free-text responses to open-ended survey questions and develop topics comparable to those found through human coding. This process, using biterm topic models (BTMs), was first used to verify and validate the results of human coding and, because of its increased speed to insights compared with that of human coding, to validate hypotheses empirically much more quickly in subsequent surveys. Analyzing free-text responses has given the IEDA confidence that open-ended survey questions provide value not previously captured. In addition to the original survey, the three subsequent ones, along with several additional projects, have been shaped by the original text-mining methods.
ABSTRACT
The Singapore Government first released their digital government blueprint in 2018 with the key message for all their agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services, especially for individual citizens. During COVID-19, Singapore government agencies introduced many COVID-19 digital initiatives resulting in more incoming inquiries from citizens to respective agencies. This surge in inquiries created the challenge on the agencies' end to meet service level agreements. One widely adopted solution is the use of chatbot technology that directly interfaces with the customer. However, several organisations have faced backlash from the citizens or customers when such chatbots cannot answer or give inappropriate answers to the questions. Hence this research takes a different approach to address this challenge using a question answering (QA) system that supports the CSOs to help answer the citizen inquiries more efficiently. This paper shares our learnings from implementing the pilot QA system;the Citizen Question Answering System (CQAS) was built using a hybrid QA approach that combines techniques from Natural Language Process QA, Knowledge-based QA and Information Retrieval QA. We also highlight the essential learnings in implementing QA systems within a government agency. The research will further share how these learnings could inform the adoption of QA systems in a government setting. The subsequent research following this paper will then focus on conducting a user study with the CSOs to validate further the benefits of this pilot QA system, which is not covered in this paper. © 2022 Owner/Author.
ABSTRACT
This paper presents the Cognitive and Social WELL-being (CoSoWELL) project that consists of two components. One is a large corpus of narratives written by over 1000 North American older adults (55+ years old) in five test sessions before and during the first year of the COVID-19 pandemic. The other component is a rich collection of socio-demographic data collected through a survey from the same participants. This paper introduces the first release of the corpus consisting of 1.3 million tokens and the survey data (CoSoWELL version 1.0). It also presents a series of analyses validating design decisions for creating the corpus of narratives written about personal life events that took place in the distant past, recent past (yesterday) and future, along with control narratives. We report results of computational topic modeling and linguistic analyses of the narratives in the corpus, which track the time-locked impact of the COVID-19 pandemic on the content of autobiographical memories before and during the COVID-19 pandemic. The main findings demonstrate a high validity of our analytical approach to unique narrative data and point to both the locus of topical shifts (narratives about recent past and future) and their detailed timeline. We make the CoSoWELL corpus and survey data available to researchers and discuss implications of our findings in the framework of research on aging and autobiographical memories under stress.
ABSTRACT
COVID-19 is an infectious disease, which was first appeared in December 2019 in Wuhan, China. This virus has spread all over the world. So in a situation like this, Twitter is helping people by giving the latest information and to connect with others. As the WHO giving health information, this paper work is an implementation of automation for extracting details of Covid-19 from the latest Tweets of Twitter Social media. Most of the people started with Negative tweets about covid19, but with increasing time people shifted towards positive and neutral comments. At some time most of the comments are about winning against coronavirus. To understand the people's opinion towards this pandemic through their tweets, we have tried to come up with an algorithm that will try to analyze the tweets using the modern computational power and some of the advanced algorithms and finally concluded at a point. Sentiment analysis using LSTM (Long Short Term Memory) which is a type of Recurrent Neural Networks, has been applied to tweets having covid19 Hash tags to see people's reactions to the pandemic. The tweets are classified and labeled as positive, negative, and neutral then visualized the result. Tweets are categorized into three classes and derive some useful patterns from them and trying to come up with some generalized algorithms so that it cannot only be applied for Covid19 or some health-related, rather apply all kind of tweets or some other social media platform such as Instagram or LinkedIn. © 2022 IEEE.
ABSTRACT
Background and aims: The COVID-19 pandemic outbreak has created severe public health crises and economic consequences across the globe. This study used text analytics techniques to investigate the key concerns of Indian citizens raised in social media during the second wave of COVID-19. Methods: In this study, we performed a sentiment and emotion analysis of tweets to understand the attitude of Indian citizens during the second wave of COVID-19. Moreover, we performed topic modeling to understand the significant issues and concerns related to COVID-19. Results: Our results show that most social media posts were in neutral tone, and the percentage of posts that showed positive sentiment was less. Furthermore, emotion analysis results show that 'Fear' and 'Surprise' were the prominent emotions expressed by the citizens. Topic modeling results reveal that 'High crowd' and 'political rallies' are the two primary topics of concern raised by Indian citizens during the second wave of COVID-19. Conclusions: Hence, Indian government agencies should communicate crisis information and combating strategies to citizens more effectively in order to minimize the fear and anxiety amongst the public.
ABSTRACT
The Covid-19 outbreak forced education into a distance modality. Professors and educators suddenly confronted unexpected challenges, including a lack of technical skills to implement efficient pedagogies in this modality. One rescuing element was social media, increasingly used inside organizations. It allows users to create content and provide valuable information on human interactions and collective behavior, mainly textual data. The objective of the current research was to identify professorial concerns after the shift to distance education and the first 15 months of confinements. Specifically, we analyzed the comments expressed in the social networks by more than 5,700 faculty members of a Mexican private university that implemented online teaching. Applying Educational Data Mining to 680 remarks retrieved from the social network, we used Voyant Tools and R programming for text and sentiment analysis. The results evidenced that the professors created a kind social network, sharing tips and digital media as educational resources, which led to a natural learning curve for developing online teaching competencies. Other relevant findings included the need to provide the professors continuous training in communication and learning management platforms to engage in ongoing discussions on topics, such as whether turning on the cameras should be compulsory during online lectures. This work's results have value to higher education institutions and professors seeking a better understanding of their requirements and decision-making to improve education delivery under current and future constraints.
ABSTRACT
Governments and healthcare organizations increasingly pay attention to social media for handling a disease outbreak. The institutions and organizations need information support to gain insights into the situation and act accordingly. Currently, they primarily rely on ground-level data, collecting which is a long and cumbersome process. Social media data present immense opportunities to use ground data quickly and effectively. Governments and HOs can use these data in launching rapid and speedy remedial actions. Social media data contain rich content in the form of people's reactions, calls-for-help, and feedback. However, in healthcare operations, the research on social media for providing information support is limited. Our study attempts to fill the gap mentioned above by investigating the relationship between the activity on social media and the quantum of the outbreak and further using content analytics to construct a model for segregating tweets. We use the case example of the COVID-19 outbreak. The pandemic has advantages in contributing to the generalizability of results and facilitating the model's validation through data from multiple waves. The findings show that social media activity reflects the outbreak situation on the ground. In particular, we find that negative tweets posted by people during a crisis outbreak concur with the quantum of a disease outbreak. Further, we find a positive association between this relationship and increased information sharing through retweets. Building further on this insight, we propose a model using advanced analytical methods to reduce a large amount of unstructured data into four key categories-irrelevant posts, emotional outbursts, distress alarm, and relief measures. The supply-side stakeholders (such as policy makers and humanitarian organizations) could use this information on time and optimize resources and relief packages in the right direction proactively.
ABSTRACT
The COVID-19 pandemic influenced people’s everyday lives because of the health emergency and the resulting socio-economic crisis. People use social media to share experiences and search for information about the disease more than before. This paper aims at analysing the discourse on COVID-19 developed in 2020 by Italian tweeters, creating a digital storytelling of the pandemic. Employing thematic analysis, an approach used in bibliometrics to highlight the conceptual structure of a research domain, different time slices have been described, bringing out the most discussed topics. The graphical mapping of these topics allowed obtaining an easily readable representation of the discourse, paving the way for novel uses of thematic analyses in social sciences.
ABSTRACT
In this research, we analyzed the Covid-19 phenomena in the USA through analysis of Twitter data related to the Covid-19 pandemic in USA. We made this analysis with Twitter data from April and May of the year 2020. What we did differently in this research was focusing on one hashtag only so that we could focus on a fixed community. Our goal is to see if there is a connection or a pattern that could be found between the different output measures and plots. To do this, we focused on the country of the USA as a use-case. The difference in this analysis is that we didn't create our dataset by downloading data generally related to Covid-19 in the USA (from multiple tags), but rather we tracked one Twitter hashtag, ensuring that we track a certain group of the population so we could be sure about our population interest calculation results. © 2021 IEEE.
ABSTRACT
With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features. © 2021 IEEE.
ABSTRACT
BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.
ABSTRACT
Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach this with algorithms, methods, and models. These include machine learning and deep learning (DL) methods, unigram and skip-gram based algorithms, as well as the Artificial Neural Network (ANN) and bag-of-word (BOW) regression model. Studies and research have revealed incoherence in polarity, model overfitting and performance issues, as well as high cost in data processing. This experiment was conducted to solve these revealing issues, by building a high performance yet cost-effective model for predicting accurate sentiments from large datasets containing customer reviews. This model uses the fastText library from Facebook's AI research (FAIR) Lab, as well as the traditional Linear Support Vector Machine (LSVM) to classify text and word embedding. Comparisons of this model were also done with the author's a custom multi-layer Sentiment Analysis (SA) Bi-directional Long Short-Term Memory (SA-BLSTM) model. The proposed fastText model, based on results, obtains a higher accuracy of 90.71% as well as 20% in performance compared to LSVM and SA-BLSTM models.
ABSTRACT
Amid the flood of fake news on Coronavirus disease of 2019 (COVID-19), now referred to as COVID-19 infodemic, it is critical to understand the nature and characteristics of COVID-19 infodemic since it not only results in altered individual perception and behavior shift such as irrational preventative actions but also presents imminent threat to the public safety and health. In this study, we build on First Amendment theory, integrate text and network analytics and deploy a three-pronged approach to develop a deeper understanding of COVID-19 infodemic. The first prong uses Latent Direchlet Allocation (LDA) to identify topics and key themes that emerge in COVID-19 fake and real news. The second prong compares and contrasts different emotions in fake and real news. The third prong uses network analytics to understand various network-oriented characteristics embedded in the COVID-19 real and fake news such as page rank algorithms, betweenness centrality, eccentricity and closeness centrality. This study carries important implications for building next generation trustworthy technology by providing strong guidance for the design and development of fake news detection and recommendation systems for coping with COVID-19 infodemic. Additionally, based on our findings, we provide actionable system focused guidelines for dealing with immediate and long-term threats from COVID-19 infodemic.
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We examine COVID-19 related topics discussed in the printed edition of the Wall Street Journal. Using text analytics and topic modeling algorithms, we discover 15 distinct topics and present differences in their sentiment (polarity) and hype (intensity of coverage) trends throughout 2020. Importantly, the hype of the topic, not the sentiment, relates to stock market returns. In particular, the hype scores for Debt market and Financial markets have the strongest positive relation to the stock market performance.
ABSTRACT
Research has shown that the temporal focus of individuals can have a real effect on behavior. In the context of the COVID-19 pandemic, this study posits that temporal focus will affect adherence behavior regarding health control measures, such as social distancing, hand washing and mask wearing, which will be manifested through the degree of spread of COVID-19. It is suggested that social media can provide an indicator of the general temporal focus of the population at a particular time. In this study, we examine the temporal focus of Twitter text data and the number of COVID-19 cases in the US over a 317-day period from the inception of the pandemic, using text analytics to classify the temporal content of 0.76 million tweets. The data is then analyzed using dynamic regression via advanced ARIMA modelling, differencing the data, removing weekly seasonality and creating a stationary time series. The result of the dynamic regression finds that past orientation does indeed have an effect on the growth of COVID-19 cases in the US. However, a present focus tends to reduce the spread of COVID cases. Future focus had no effect in the model. Overall, the research suggests that detecting and managing temporal focus could be an important tool in managing public health during a pandemic.