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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.
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Introduction: The COVID-19 pandemic has triggered a massive acceleration in the use of virtual and video-visits. As more patients and providers engage in video-visits over varied digital platforms, it is important to understand how patients assess their providers and the video-visit experiences. We also need to examine the relative importance of the factors that patients use in their assessment of video-visits in order to improve the overall healthcare experience and delivery. Methods: A data set of 5149 reviews of patients completing a video-visit was assembled through web scraping. Sentiment analysis was performed on the reviews and topic modeling was used to extract latent topics embedded in the reviews and their relative importance. Results: Most patient reviews (89.53%) reported a positive sentiment towards their providers in video-visits. Seven distinct topics underlying the reviews were identified: bedside manners, professional expertise, virtual experience, appointment scheduling and follow-up process, wait times, costs, and communication. Communication, bedside manners and professional expertise were the top factors patients alluded to in the positive reviews. Appointment-scheduling and follow-ups, wait-times, costs, virtual experience and professional expertise were important factors in the negative reviews. Discussion: To improve the overall experience of patients in video-visits, providers need to engage in clear communication, grow excellent bedside and webside manners, promptly attend the video-visit with minimal delays and follow-up with patients after the visit.
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Investors' decisions to invest in stock markets can be significantly influenced by online rumors generated by certain companies or influencers. The current understanding of how certain sentimental features can help increase the prediction capabilities of online rumors is still in its fancy stage. This study explored the types of topics and emotions found in rumor messages and how they are associated with investors' decisions to invest in stocks. We also investigated the potential of using these emotions in predicting investors' intention to invest in stock markets. The sentimental features consisted of users' emotions (anger, fear, sadness, joy, and trust) and polarity (positive, negative, and neutral). A topic modeling approach was applied to identify logical associations between different sentimental features of rumors on Twitter. The results showed that rumors tweets associated with investors' intention to invest were linked to the joy and trust sentiments, while the anger and fear sentiments were linked to no intention to invest. The results showed that these emotions can be used in predicting the impact of online rumors on investors' investment decisions. The prediction model can be useful for stock market prediction by enabling managers and researchers to analyze and assess the magnitudinal impact of rumors on certain investment decisions. The outcomes can also help decision and policy makers to take the required actions to prevent possible financial instability due to COVID-19 or other future events.
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This study focused on the impacts of COVID-19 on SDG4 to resolve inequality through education and explored UNESCO's educational practices. We used text mining to analyze strategic and crisis-related reports published by UNESCO from 2003 to 2021 and LDA topic modeling analysis was used to determine their latent contexts. Two topics related to education strategies were 'sustainable development' and 'system and organization'. According to the themes, non-formal, formal and informal learning and skills and TVET topics were derived for lifelong learning, school and teacher, emergency and peace, policy and framework in the theme of crisis and conflict. Finally, latent topics during each MDGs, SDGs and COVID-19 period showed insignificant changes. However, compared to before the 2014 MDGs, strategic discourses tended to be discussed in detail. Moreover, we noted the change in global discourse from globalization to digital innovation. After the pandemic, the international community has emphasized the role of teachers and improved internet access for interaction. Such recommendations were intended to bridge the gap between countries including developing countries. As an alternative, UNESCO has suggested various partnership practices but there are nevertheless limitations that cannot be solved through a partnership or educational support. Therefore, reaching SDG4 requires global efforts to change the world by coordinating specific target countries and various social factors surrounding the countries' interior and exterior. © 2023 by the authors.
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Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication-language style, contents and user activity-in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (- 3.45%) as well as for social disengagement such as social references (- 8.63%) and 2nd person pronouns (- 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (- 14.91%), and Reaching Out to users (- 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic.
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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.
Subject(s)
COVID-19 , Social Media , Humans , COVID-19 Vaccines , COVID-19/prevention & control , Canada , Certification , AttitudeABSTRACT
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.
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As COVID-19 emerged and prolonged, various changes have occurred in our lives. For example, as restrictions on daily life are lengthening, the number of people complaining of depression is increasing. In this paper, we conduct a sentiment analysis by modelling public emotions and issues through social media. Text data written on Twitter is collected by dividing it into the early and late stages of COVID-19, and emotional analysis is performed to reclassify it into positive and negative tweets. Therefore, subject modelling is performed with a total of four datasets to review the results and evaluate the modelling results. Furthermore, topic modelling results are visualised using dimensional reduction, and public opinions on COVID-19 are intuitively confirmed by generating representative words consisting of each topic in the word cloud. Additionally, we implement a COVID-chatbot that provides a question-and-answer service on COVID-19 and verifies the performance in our experiments.
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The sharing economy represented by Airbnb has evolved rapidly. It is particularly important to identify and understand how consumer concerns change over time. As a result, this study employs structural topic modelling using room type and time as covariates to extract topics from 896,658 Airbnb reviews in London and to observe the variation in the prevalence of topics over time. The findings show that the topic proportion changed relatively sharply in the early years of Airbnb (2010-2013) and during the COVID-19 pandemic (2020-2022), but relatively smoothly in the middle period (2014-2019). This research also discovered that the proportion of topics on customers' special experiences has been decreasing while the proportion of topics on their overall experience has been increasing. This shift could be attributed to an increase in the number of professional hosts, which has accelerated the standardisation of the Airbnb service.
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Combining computational and qualitative methods, this research presents a novel approach to the analysis of China's digital diplomacy. The study explores the main strategic narratives disseminated by the Chinese Communist Party on Twitter during the first year of the COVID-19 pandemic. To identify the narratives, a sequential design was conducted. First, topic modelling was implemented to a sample of 189,708 tweets in English published by 163 Chinese authorities from January 1st, 2020, to March 11th, 2021. Second, the strategic narratives framework was applied to distinguish thematic and structural patterns among the most representative tweets of the main topics revealed. The findings expose how China tried to rationalise challenging events in accordance with its pre-established system and identity narratives. The antagonism to the West, the promotion of a new style of global leadership, the rejection of criticism, and the legitimation of projects abroad characterised China's digital endeavours to influence international audiences. © 2023 Australian and New Zealand Communication Association.
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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.
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The current study aimed to explore the public understanding of COVID-19 vaccines and the social representations emerging from a corpus of user-generated comments on YouTube videos posted during the year following the World Health Organization's declaration of the novel coronavirus as pandemic. We used Structural Topic Modelling to process the text and identified a 10-topic solution as the best to represent the corpus of text data. The exploration of the topics showed a complex landscape of social representations underlying a plurality of perspectives, which we interpreted as reflecting different users' needs to make sense of the unprecedented events. Implications for theory, future research, and intervention for health psychology and policy are discussed. © The Author(s) 2023.
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Purpose: This study aims to conduct a "real-time" investigation with user-generated content on Twitter to reveal industry challenges and business responses to the coronavirus (Covid-19) pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded. Design/methodology/approach: With 94,340 tweets collected between October 2019 and May 2020 by a programmed Web scraper, unsupervised machine learning approaches such as structural topic modelling are applied. Originality/value: This study contributes to the literature on business response during crises providing for the first time a study of using unstructured content on social media for industry-level analysis in the hospitality context. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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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.
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The World Health Organization (WHO) has declared Covid-19 as a pandemic since March 11, 2020. The emergence of the Covid-19 pandemic has caused a lot of discussion around the world. Sentiment Analysis and Topic Modeling using Latent Dirichlet Allocation (LDA) can be used to extract patterns or information from a set of texts. This study uses a Systematic Literature Review (SLR) to see what the most dominant topics are discussed during the Covid-19 pandemic and find out research gaps for further research about Sentiment Analysis and Topic Modeling using Latent Dirichlet Allocation (LDA). The articles used are limited to the article publication period, February 2020 to July 2021. The results of the review show that case handling (lockdown, international airports closure), conspiracy issues and fake news, number of daily case reports, the importance of covid prevention, Covid-19 vaccination policy, economic downturn, transportation systems, learning systems, and new policies for each country were the most discussed topics from March 2020 to January 2021. © 2023 Author(s).
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Data: This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. Methodology: On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news. Results: Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Conclusion: Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.
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Hotel industry is the one which has confronted the unprecedented effect of the coronavirus disease 2019 (COVID-19) pandemic to significant social and economic risks. The COVID-19 pandemic has challenged the tourism across the globe and impacted hospitality in hotel industry severely. This study aims to assess customer satisfaction by carrying sentiment analysis and topic modelling over customer reviews on the hospitality provided by hotels in different continents during January to September 2020, i.e. the COVID-19 pandemic. We formulate an improved new scale of metrics to categorize customer satisfaction assessed by sentiment analysis in an elaborate way. Topic modelling was deployed to understand various topics most often discussed by customers. We find that North America and Europe could perform up to customer expectation. In Asia, Sri Lanka did well, Indonesia could maintain its customer satisfaction, while India consistently improved the satisfaction level. We identified 12 most discussed topics, and main reasons of dissatisfaction appear in staff, service, room, cleanliness, slow booking, and pandemic response by hotel. Findings of this study will help senior managers of hotels of developed as well as developing countries in providing new and effective services that can satisfy customers and restore their confidence. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
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Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID- 19 pandemic. However, most of the analyses relied on closed-ended questions, which limited student involvement. To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis. This study also looked at students' emotional states during and after the COVID-19 pandemic. In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes. Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study. Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life. Students were less accepting of online learning. As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication. © 2023 Tech Science Press. All rights reserved.
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Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.
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Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID- 19 pandemic. However, most of the analyses relied on closed-ended questions, which limited student involvement. To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis. This study also looked at students' emotional states during and after the COVID-19 pandemic. In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes. Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study. Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life. Students were less accepting of online learning. As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication. © 2023 Tech Science Press. All rights reserved.