Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.

BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends. RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

Predictors of Criminal Sentiments Scale-Modified Scores in Outpatients with Legal System Involvement.

The Criminal Sentiments Scale-Modified (CSS-M) has been widely used as a measure of criminal attitudes. This analysis examined CSS-M scores in a large sample of outpatients with serious mental illnesses and a criminal legal system history. We compared total and subscale scores in our sample to scores from two other previously published U.S. studies in which the CSS-M was used, and evaluated associations between total CSS-M score and nine variables (age, educational attainment, gender, race, marital status, employment status, diagnostic category, substance use disorder comorbidity, and adverse childhood experiences (ACE) score). Scores were higher than in two prior U.S. studies involving other types of samples. Independently significant predictors of higher CSS-M scores included being younger (P < .001), having a higher ACE score (P < .001), being male (P = 03), not identifying as White (P < 001), not having a psychotic disorder (P < 001), and having a comorbid substance use disorder (P = 002). Future research should test the hypothesis that these factors increase risk for arrest and that arrest events, and subsequent criminal legal system involvement, are characterized by negative experiences and perceptions of poor procedural justice, which in turn underpin the negative opinions referred to as "criminal sentiments" or criminal attitudes.

Public sentiments and the influence of information-seeking preferences on knowledge, attitudes, death conversation, and receptiveness toward palliative care: results from a nationwide survey in Singapore.

Background: Low awareness about palliative care among the global public and healthcare communities has been frequently cited as a persistent barrier to palliative care acceptance. Given that knowledge shapes attitudes and encourages receptiveness, it is critical to examine factors that influence the motivation to increase knowledge. Health information-seeking from individuals and media has been identified as a key factor, as the process of accessing and interpreting information to enhance knowledge has been shown to positively impact health behaviours. Objective: Our study aimed to uncover public sentiments toward palliative care in Singapore. A conceptual framework was additionally developed to investigate the relationship between information-seeking preferences and knowledge, attitudes, receptiveness of palliative care, and comfort in death discussion. Design and Methods: A nationwide survey was conducted in Singapore with 1226 respondents aged 21 years and above. The data were analysed through a series of hierarchical multiple regression to examine the hypothesised role of information-seeking sources as predictors. Results: Our findings revealed that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. It further showed that while information-seeking from individuals and media increases knowledge, attitudes and receptiveness to palliative care, the comfort level in death conversations was found to be positively associated only with individuals, especially healthcare professionals. Conclusion: Our findings highlight the need for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. As Asians view death as a taboo topic that is to be avoided at all costs, it is necessary to adopt multipronged communication programs to address those fears. It is only when the larger communicative environment is driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

A nationwide survey to understand public sentiments and the extent that information-seeking preferences can increase knowledge, attitudes, receptiveness of palliative care, and comfort level in death discussion in Singapore Low awareness of palliative care is a barrier that persistently hinders palliative care acceptance among populations in developing and developed countries. As knowledge shapes attitudes and encourages receptiveness, it is vital that researchers uncover factors that influence the motivation to increase knowledge. Health information-seeking is a factor that deserves greater attention in palliative care research because the process of seeking out information on health concerns from other people or the media can greatly increase individuals' knowledge. As such, this nationwide survey involving 1226 participants was carried out in Singapore to understand the public sentiments toward palliative care. It further statistically analyzed if information-seeking (from individuals and the media) will increase knowledge, attitudes, receptiveness toward palliative care, and comfort level in death discussion. Our findings indicated that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. Furthermore, while information-seeking from individuals and media increases knowledge, attitudes, and receptiveness to palliative care, people are only comfortable to engage in death discussion with individuals, especially healthcare professionals. Exposure to media alone is not enough to encourage individuals to want to talk about end-of-life issues including palliative care. As Asians view death as a taboo topic, it is important for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. A multipronged communication program is therefore needed to address these fears. It is only when the larger communicative environment driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data.

BACKGROUND: Twitter has become a popular platform for individuals to broadcast their daily experiences and opinions on a wide range of topics and emotions. Tweets from patients with cancer could offer insights into their needs. However, limited research has been conducted using Twitter data to understand the needs of patients with cancer despite the substantial amount of health-related data posted on the platform daily. OBJECTIVE: This study aimed to uncover the potential of using Twitter data to understand the perspectives and experiences of patients with thyroid cancer at a global level. METHODS:  This retrospective descriptive study collected tweets relevant to thyroid cancer in 2020 using the Twitter scraping tool. Only English-language tweets were included, and data preprocessing was performed to remove irrelevant tweets, duplicates, and retweets. Both tweets and Twitter users were manually classified into various groups based on the content. Each tweet underwent sentiment analysis and was classified as either positive, neutral, or negative. RESULTS: A total of 13,135 tweets related to thyroid cancer were analyzed. The authors of the tweets included patients with thyroid cancer (3225 tweets, 24.6%), patient's families and friends (2449 tweets, 18.6%), medical journals and media (1733 tweets, 13.2%), health care professionals (1093 tweets, 8.3%), and medical health organizations (940 tweets, 7.2%), respectively. The most discussed topics related to living with cancer (3650 tweets, 27.8%), treatment (2891 tweets, 22%), diagnosis (1613 tweets, 12.3%), risk factors and prevention (1137 tweets, 8.7%), and research (953 tweets, 7.3%). An average of 36 tweets pertaining to thyroid cancer were posted daily. Notably, the release of a film addressing thyroid cancer and the public disclosure of a news reporter's personal diagnosis of thyroid cancer resulted in a significant escalation in the volume of tweets. From the sentiment analysis, 53.5% (7025/13,135) of tweets were classified as neutral statements and 32.7% (4299/13,135) of tweets expressed negative emotions. Tweets from patients with thyroid cancer had the highest proportion of negative emotion (1385/3225 tweets, 42.9%), particularly when discussing symptoms. CONCLUSIONS:  This study provides new insights on using Twitter data as a valuable data source to understand the experiences of patients with thyroid cancer. Twitter may provide an opportunity to improve patient and physician engagement or apply as a potential research data source.

The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis.

Background: XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant. Objectives: This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA). Methods: This experiment involved the collection of Twitter data through the keywords, "XBB.1.5″, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral. Results: A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments. Conclusion: Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.

How does air pollution affect the stock market performance? Evidence from China.

Given its broad impact on human society, air pollution could become a non-economic factor affecting the stock market. But the impact of air pollution on the stock market performance has not received enough attention. This study examines the influence and potential mechanism of air pollution on stock market performance based on the panel data of 1344 A-share listed firms in China covering the period 2013-2019. The result shows that air pollution can negatively affect stock market performance. Second, heterogeneity analysis creatively points out that firms with less analysts, smaller size, stated-owned ownership, polluting related industry are more vulnerable to the negative effects of air pollution. Finally, the result also reveals a mechanism that air pollution could worsen the stock market by depressing investors' sentiments. The above findings enrich current research related to the impact of air pollution on stock market performance and also provide a new perspective for investors to make stock investment decisions.

[Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic]. / Emotionalität in der COVID-19-Krisenkommunikation von Behörden und unabhängigen Expert*innen auf Twitter : Eine Sentiment-Analyse für das erste Pandemiejahr.

BACKGROUND: At the beginning of the COVID­19 pandemic in Germany, there was great uncertainty among the population and among those responsible for crisis communication. A substantial part of the communication from experts and the responsible authorities took place on social media, especially on Twitter. The positive, negative, and neutral sentiments (emotions) conveyed there during crisis communication have not yet been comparatively studied for Germany. STUDY AIM: Sentiments in Twitter messages from various (health) authorities and independent experts on COVID­19 will be evaluated for the first pandemic year (1 January 2020 to 15 January 2021) to provide a knowledge base for improving future crisis communication. MATERIAL AND METHODS: From n = 39 Twitter actors (21 authorities and 18 experts), n = 8251 tweets were included in the analysis. The sentiment analysis was done using the so-called lexicon approach, a method within the social media analytics framework to detect sentiments. Descriptive statistics were calculated to determine, among other things, the average polarity of sentiments and the frequencies of positive and negative words in the three phases of the pandemic. RESULTS AND DISCUSSION: The development of emotionality in COVID­19 tweets and the number of new infections in Germany run roughly parallel. The analysis shows that the polarity of sentiments is negative on average for both groups of actors. Experts tweet significantly more negatively about COVID­19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.

Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the COVID-19 pandemic.

Background: Public sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management. Objective: This study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management. Methods: This study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic. Results: The research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments. Discussion: Since the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.

Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation.

The increasing reliance on mobile health for managing disease conditions has opened a new frontier in digital health, thus, the need for understanding what constitutes positive and negative sentiments of the various apps. This paper relies on Embedded Deep Neural Networks (E-DNN), Kmeans, and Latent Dirichlet Allocation (LDA) for predicting the sentiments of diabetes mobile apps users and identifying the themes and sub-themes of positive and negative sentimental users. A total of 38,640 comments from 39 diabetes mobile apps obtained from the google play store are analyzed and accuracy of 87.67 % ± 2.57 % was obtained from a 10-fold leave-one-out cross-validation. This accuracy is 2.95 % - 18.71 % better than other predominant algorithms used for sentiment analysis and 3.47 % - 20.17 % better than the results obtained by previous researchers. The study also identified the challenges of diabetes mobile apps usage to include safety and security issues, outdated information for diabetes management, clumsy user interface, and difficulty controlling operations. The positives of the apps are ease of operation, lifestyle management, effectiveness in communication and control, and data management capabilities.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

NAFLD vs MAFLD: South Asian NAFLD Patients don't Favor Name Change.

Aim: There have been vociferous attempts to change the name of Nonalcoholic Fatty Liver Disease (NAFLD) to Metabolic Associated Fatty Liver Disease (MAFLD). Of the many arguments put forth in support of this, an important one is the presumed demand by patient groups insisting on the change. However, this claim does not have credible evidence to support it. Therefore, we decided to conduct a survey among South Asian NAFLD patients to understand their perspectives with regard to the change in nomenclature. Materials and Methods: The study was conducted at multiple centers across South Asia from January 2021 to June 2021. Patients were surveyed using an 8-question survey questionnaire and responses were categorized by multiple-choice format. Results: Of 218 patients surveyed, 80.3% of the patients were not aware of the entity "NAFLD" before they were first diagnosed. Although 74.3% of patients admitted to being questioned about alcohol intake at the time of the first diagnosis, 75.9% of female patients were not questioned regarding this. After being labelled NAFLD, 92.1% of patients were never questioned again about alcohol intake. While 86.3% of patients found the term "NAFLD" consoling, 83% did not feel that "Non" in NAFLD trivialized their problem. In addition, only 6.9% of patients were scared of developing cardiovascular disease. Conclusion: The term "NAFLD" destigmatizes patients of the taboo associated with alcohol use. It was found to be consoling to most patients and they did not feel it trivialized their problem. A change of name without considering patients' perspectives and peculiarities specific to different populations will have enormous ramifications for both patients and physicians. Clinical significance: Our survey clearly shows that patients are happy with the term "NAFLD" and it effectively destigmatizes them from the taboo of alcohol. This would lead to higher compliance with management and greater patient participation in future studies and trials. How to cite this article: Singh SP, Anirvan P, Butt AS, et al. NAFLD vs MAFLD: South Asian NAFLD Patients don't Favor Name Change. Euroasian J Hepato-Gastroenterol 2022;12(Suppl 1):S1-S4.

Health professionals' sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis.

BACKGROUND: Psychiatric hospitals are increasingly being digitalised. Digitalisation often requires changes at work for health professionals. A positive attitude from health professionals towards technology is crucial for a successful and sustainable digital transformation at work. Nevertheless, insufficient attention is being paid to the health professionals' sentiments towards technology. OBJECTIVE: This study aims to identify the implemented technologies in psychiatric hospitals and to describe the health professionals' sentiments towards these implemented technologies. METHODS: A text-mining analysis of semi-structured interviews with nurses, physicians and psychologists was conducted. The analysis comprised word frequencies and sentiment analyses. For the sentiment analyses, the SentimentWortschatz dataset was used. The sentiments ranged from -1 (strongly negative sentiment) to 1 (strongly positive sentiment). RESULTS: In total, 20 health professionals (nurses, physicians and psychologists) participated in the study. When asked about the technologies they used, the participating health professionals mainly referred to the computer, email, phone and electronic health record. Overall, 4% of the words in the transcripts were positive or negative sentiments. Of all words that express a sentiment, 73% were positive. The discussed technologies were associated with positive and negative sentiments. However, of all sentences that described technology at the workplace, 69.4% were negative. CONCLUSIONS: The participating health professionals mentioned a limited number of technologies at work. The sentiments towards technologies were mostly negative. The way in which technologies are implemented and the lack of health professionals' involvement seem to be reasons for the negative sentiments.

Validity testing of the conspiratorial thinking and anti-expert sentiment scales during the COVID-19 pandemic across 24 languages from a large-scale global dataset.

In this study, we tested the validity across two scales addressing conspiratorial thinking that may influence behaviours related to public health and the COVID-19 pandemic. Using the COVIDiSTRESSII Global Survey data from 12 261 participants, we validated the 4-item Conspiratorial Thinking Scale and 3-item Anti-Expert Sentiment Scale across 24 languages and dialects that were used by at least 100 participants per language. We employed confirmatory factor analysis, measurement invariance test and measurement alignment for internal consistency testing. To test convergent validity of the two scales, we assessed correlations with trust in seven agents related to government, science and public health. Although scalar invariance was not achieved when measurement invariance test was conducted initially, we found that both scales can be employed in further international studies with measurement alignment. Moreover, both conspiratorial thinking and anti-expert sentiments were significantly and negatively correlated with trust in all agents. Findings from this study provide supporting evidence for the validity of both scales across 24 languages for future large-scale international research.

Urban greenspace helps ameliorate people's negative sentiments during the COVID-19 pandemic: The case of Beijing.

The COVID-19 pandemic has had negative effects on people's mental health worldwide, especially for those who live in large cities. Studies have reported that urban greenspace may help lessen these adverse effects, but more research that explicitly considers urban landscape pattern is needed to understand the underlying processes. Thus, this study was designed to examine whether the resident sentiments in Beijing, China changed before and during the pandemic, and to investigate what urban landscape attributes - particularly greenspace - might contribute to the sentiment changes. We conducted sentiment analysis based on 25,357 geo-tagged microblogs posted by residents in 51 neighborhoods. We then compared the resident sentiments in 2019 (before the COVID-19) with those in 2020 (during the COVID-19) using independent sample t-tests, and examined the relationship between resident sentiments and urban greenspace during the COVID-19 pandemic phases using stepwise regression. We found that residents' sentiments deteriorated significantly from 2019 to 2020 in general, and that urban sentiments during the pandemic peak times showed an urban-suburban trend that was determined either by building density or available greenspace. Although our analysis included several other environmental and socioeconomic factors, none of them showed up as a significant factor. Our study suggests the effects of urban greenspace and building density on residents' sentiments increased during the COVID-19 pandemic and that not all green spaces are equal. Increasing greenspace, especially within and near neighborhoods, seems critically important to helping urban residents to cope with public health emergencies such as global pandemics.

Human sentiments monitoring during COVID-19 using AI-based modeling.

The whole world is facing health challenges due to wide spread of COVID-19 pandemic. To control the spread of COVID-19, the development of its vaccine is the need of hour. Considering the importance of the vaccines, many industries have put their efforts in vaccine development. The higher immunity against the COVID can be achieved by high intake of the vaccines. Therefore, it is important to analysis the people's behaviour and sentiments towards vaccines. Today is the era of social media, where people mostly share their emotions, experience, or opinions about any trending topic in the form of tweets, comments or posts. In this study, we have used the freely available COVID-19 vaccines dataset and analysed the people reactions on the vaccine campaign using artificial intelligence methods. We used TextBlob() function of python and found out the polarity of the tweets. We applied the BERT model and classify the tweets into negative and positive classes based on their polarity values. The classification results show that BERT has achieved maximum values of precision, recall and F score for both positive and negative sentiment classification.

Fat stigma and body objectification: A text analysis approach using social media content.

This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications.

Cross Country Determinants of Investors' Sentiments Prediction in Emerging Markets Using ANN.

The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor-current health expenditure as a percentage of GDP, USA IS predictor-spread, and Macro-factor GDP-annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S&P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, diverse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.

Understanding COVID-19 response by twitter users: A text analysis approach.

COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include "cases", "home", "people" and "help". We identified "30" such topics and categorized them into "three" themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people.

A qualitative study of developers' discussions of their problems and joys during the early COVID-19 months.

Many software developers started to work from home on a short notice during the early periods of COVID-19. A number of previous papers have studied the wellbeing and productivity of software developers during COVID-19. The studies mainly use surveys based on predefined questionnaires. In this paper, we investigate the problems and joys that software developers experienced during the early months of COVID-19 by analyzing their discussions in online forum devRant, where discussions can be open and not bound by predefined survey questionnaires. The devRant platform is designed for developers to share their joys and frustrations of life. We manually analyze 825 devRant posts between January and April 12, 2020 that developers created to discuss their situation during COVID-19. WHO declared COVID-19 as pandemic on March 11, 2020. As such, our data offers us insights in the early months of COVID-19. We manually label each post along two dimensions: the topics of the discussion and the expressed sentiment polarity (positive, negative, neutral). We observed 19 topics that we group into six categories: Workplace & Professional aspects, Personal & Family well-being, Technical Aspects, Lockdown preparedness, Financial concerns, and Societal and Educational concerns. Around 49% of the discussions are negative and 26% are positive. We find evidence of developers' struggles with lack of documentation to work remotely and with their loneliness while working from home. We find stories of their job loss with little or no savings to fallback to. The analysis of developer discussions in the early months of a pandemic will help various stakeholders (e.g., software companies) make important decision early to alleviate developer problems if such a pandemic or similar emergency situation occurs in near future. Software engineering research can make further efforts to develop automated tools for remote work (e.g., automated documentation).

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach.

The study investigates the asymmetric effect of temperature, exchange rate, metals (rare metals and electrical conductors), and investor sentiments on solar stock price performance in China. The novel econometric techniques, i.e., QARDL (quantile autoregressive distributive lag) approach and Granger causality-in-quantiles to analyze the results. In both short- and long-run estimations, the findings suggest that rare metals (cadmium, germanium, indium, and selenium) and electrical conductors (silver, aluminum, and copper) have significant and positive linkage with solar energy stocks at different quantiles based on bullish, bearish, and normal market conditions. On the other hand, negative effects are found for temperature, RMB exchange rate, and investor sentiments in both the short- and long-run. In the short run, the effect of exchange rate varies across different quantiles but it confines to only lower quantiles (bearish market condition) in the longer run. Solar stocks are more prone to investor sentiments under higher quantiles (bullish market conditions). Lastly, we find that temperature is not merely a behavioral anomaly for the solar energy market as it spreads across middle quantiles (normal market conditions) in the longer run. The findings of Granger causality in quantiles further confirm the results of QARDL.