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This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

In the digital era individuals are overwhelmed by huge amount of readily available information. The information provided at the time of COVID-19 crisis is increasingly available. The purpose of this paper was to investigate individuals’ perceived feelings due to the plethora of information during COVID-19 pandemic in Greece in Spring 2020.

This study was conducted through a Web-based questionnaire survey posted on the Google Forms platform. The questionnaire consisted of closed-ended, seven-point Likert-scale questions. The data collected were subjected to a principal component analysis. The retained principal components (PCs) were subjected to statistical analysis between genders and among age groups and professional status with the nonparametric criteria Mann–Whitney U and Kruskal–Wallis.

Responses by 776 individuals were obtained. Seventeen original variables from the questionnaire were summarized into three PCs that explained the 71.7% of total variance: “affective disorders,” “uncertainty issues and inaccurate information worries” and “satisfaction and optimism.” Participants partly agree that the received amount of information on the disease caused them feelings of uncertainty about the future and worries about relatives’ lives, but also satisfaction with developments in the country. Females seem to experience stronger perceived feelings of “affective disorders” (p < 0.001) and reported higher degree of agreement about “uncertainty issues and inaccurate information worries.”

The recorded feelings caused by the volume of available information may have forced people accept the necessary precautionary behavioral changes that had contributed to the Greek success in preventing spread of the disease in Spring 2020.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

This paper aims to show the pragmatic studies that examine whether novel COVID-19 affects the national and international stock markets and reinforces the existing literature by highlighting the factors that are resultant from COVID 19.

The systematic literature review and bibliometric approach have been used in the study covering 585 selected articles published in journals of high repute from January 2020 to January 2022. The process of bibliometric analysis has been divided into three stages, namely, assembling, arranging and assessing. From the Scopus database, one of the most reliable and authentic database total of 585 records were collected, out of which 12 were specifically focused on communities, and information gathered in the comma-separated value documents design was compared and interpreted based on year, document types, subject area, country and research fields with the help of graphs and pie charts. The study has analyzed fact-based and reliable studies to draw inferences from existing literature regarding the pandemic impacting the financial markets. In the extant study, an attempt has been made to explore the factors that are resultant from the COVID-19 pandemic and affects the stock market performance, which can be further classified into a few common factors by using factor analysis.

It originated from the majority of the studies that the stock market retorted destructively to the upsurge in the figure of COVID-19 cases and fatalities. It also emphasized that the market has reacted differently in comparison to earlier catastrophes such as the great depression of 2008 and the Spanish flu. Various factors such as fear of losing capital, standstill economy, lower valuation, increased mortality rate, halt in business operations, retrenchment, trade war, liquidity issues, panic buying and selling, digitalization, negative media coverage, government interference, financial behavior of investors, hoarding of COVID supplies, promotion of start-up in health-care and education sector, news bulletins, prevention campaigns, use of medical devices and COVID-19 vaccination, etc. have been conferred from the studies that have an immediate consequence on the actions of investors in the stock market. It was further highlighted in the study that the Indian stock market has been less explored in respect of implications of COVID-19 contagion as the majority of studies were based on either international stock exchanges or combinations of varied nation’s stock markets. It was witnessed in the interpretation section that the number of studies is increasing at a fast pace as new variants of COVID-19 are emerging over time. Significant contribution has been done in enhancing the literature on COVID-19 and the stock market by China and the USA. The maximum contribution in this domain has been done in the form of articles in the present literature. Few studies were focusing on communities, so the present study will try to fill this research gap to some extent.

This conceptual paper is demarcated by unsatisfactory analyses of writings from multi-discipline to get a comprehensive scope of notional understanding. Furthermore, there is a perchance that some other imperative phenomena or variables that prejudiced trading bustle have not been captured by present reviews of research papers. The influences of other macroeconomic variables should be explored to understand the concrete results of this pandemic.

Most of the studies were based on foreign stock exchanges, so there is an opportunity to explore the Indian stock market concerning the implications of the coronavirus pandemic. In the literature, it was examined that short-term studies have been undertaken, which cannot determine the long-term implications of COVID-19. Over time, besides COVID-19, various other factors have started impacting the stock market, so it has become difficult to examine the influence of COVID-19 on the stock market in isolation.

The study will be helpful for future learnings in the arena of the stock market as it provides vast exposure to the present literature related to the impact of COVID-19 on economic markets. On the other hand, investors will also become aware of factors that are resultant of COVID-19 and will take the right decisions to save their investments in light of pandemic implications. The extensive review of studies will also help enterprising communities to take judicial steps to remain active in the period of economic slowdown.

The paper provides significant implications to the investors in the stock market, and it will provide useful insight to improve their returns on their portfolios. The learning from the study will help investors to take fruitful decisions considering the uncertainty during the pandemic period. The inferences drawn from rich existing literature will be guiding enterprises to take timely actions to avoid the situation of loss in the market and adapt new models to ensure continuity of business operations. Different markets had reacted differently, so investors need to be cautious before taking trading decisions.