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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.

This study aims to investigate the level of reporting compliance in terms of content elements, measure to what extent each content element of the integrated reporting (IR) framework is linked to value creation and demonstrate the relationship between the level of compliance and value creation linkages.

The sample for this study consists of 12 companies, 11 of which are public and 1 is non-public. The data is obtained from the Integrated Reporting Turkey Network founded in 2015 in Turkey. This study applies a holistic approach integrating two different content analysis methods. First, a multi-weighted scoring system is constructed by using the IR content elements and the previously developed indexes in the literature. Second, in-depth, sentence-by-sentence content analysis is used to determine the relation between the content elements and value creation.

The results of the multi-weighted scoring system indicate a high level of compliance in the banking sector. On the other hand, the scores of the content analysis demonstrate higher scores in the disclosures of “basis of preparation and presentation”, “organizational overview and external environment”, “strategy and resource allocation”, “performance” and “business model” elements, while lower scores in the elements of “risk and opportunities” and “outlook.” The lowest compliance level associated with lower content analysis scores may indicate a low level of value creation potential. Consequently, this two-stage scoring is critical, as it clarifies the relation between compliance level and the explanatory power of each content element from a value creation perspective.

This study aims to support the policymakers and regulators in highlighting the importance of measuring and reporting value. Furthermore, it intends to encourage companies to produce reports that increase the value relevance of accounting information to contribute to the development of capital markets. The current literature includes research that mainly concentrates only on the quality or extent of IR disclosure practices. This study offers a combined analysis that helps to determine at what level a company has accomplished the expectations of the International Integrated Reporting Council in terms of both the content and the value creation potential.

The purpose of this study was to perform sentiment analysis and analyze the growth and popularity of Drupal, Joomla and WordPress on YouTube over a four-year period. This included identifying the most liked and commented videos for each content management system (CMS), ranking the CMSs based on the number of positive comments they received, and using natural language processing techniques to identify the top ten most frequently appearing words in videos about the CMSs.

The data for assessing the features of the videos of Drupal, WordPress and Joomla was extracted using Webometric Analyst version 4.4. with the help of the YouTube application programming interface key for videos on the selected CMSs uploaded from 2019 to 2022. The extraction of comments and sentiment analysis for the relevant videos was done using Mozdeh.

This study scrutinized 371, 234 and 313 videos of WordPress, Joomla and Drupal on YouTube. The findings reveal that there is a chronological growth of videos of the three CMSs in four years and till the present time, WordPress has the highest number of videos followed by Drupal and then Joomla. Regarding the ranking of highly liked videos, WordPress again wins the list with the highest number of likes in its videos followed by Drupal and then Joomla. For analyzing sentiments of the total comments extracted 123,409 for WordPress, 1,790 for Joomla and 1,783 for Drupal, respectively, WordPress receives the highest average positive comments followed by Drupal then Joomla. In top word frequency, the word “thank” highly occurs and viewers are asking for more tutorial videos.

To the best of the authors’ knowledge, this study is the first attempt for analyzing the sentiments of WordPress, Drupal and Joomla using Mozdeh software within the concerning period.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

This paper aims to analyse how financial literacy (FL) is conceptualised and operationalised in the Indian context.

A systematic literature review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Thirty-six articles published between 2010 and 2020 were considered for analysis. The FL conceptualisation was examined based on knowledge, ability, skill, attitude and confidence elements. The FL operationalisation was analysed using the modified version of the Organisation for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment (PISA) 2012 model for organising the domain for an assessment framework.

The findings indicate that, despite offering operationalisation details of the FL, 13 out of 36 studies did not include a conceptual definition of FL. Of the 23 studies that mentioned a conceptual definition, 87% are primarily focused on the “knowledge” element and only 39% have combined knowledge, ability/skill and attitude elements in defining FL. As in the developed countries, the Indian studies also preferred investment/saving-related contents in their FL measures. The volume of content focusing on the financial landscape is meagre amongst the FL measures used in India and developed countries. The survey instruments of most studies have been designed in the individuals’ context but have failed to measure the extent to which individuals apply the knowledge in performing their day-to-day financial transactions. Further, it was found that 20 out of 36 studies did not convert the FL level of their target groups into a single indicator or operational value.

To the best of our knowledge, this is the first study that explores the FL’s assessment practices in India. Further, this study offers new insights by comparing the contents of FL measures used in Indian studies with those used in developed countries.

This study explored the influence of Dervin’s sensemaking methodology (SMM).

Citation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.

The influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.

This study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.

The purpose of this study is to give an informative map of sustainable architectural education by focusing on publishing trends, prominent publications, prolific contributors, research challenges and future research prospects. As a consequence, an efficient framework for collecting significant knowledge and identifying prevalent topics in sustainable architectural education towards more sustainable environments at the urban and building scales may be provided.

The methodology adopted in this study is based on examining the subjects of many consecutive rounds of the Passive and Low Energy Architecture (PLEA) conference, with an emphasis on the most recent five rounds. The PLEA's official website served as the main source for gathering all proceedings. Earlier rounds from 2016 to 2020, which cover a time span of five years, were utilized to analyze patterns during that time period. The rationale for selecting this time period was the limited availability of data required to observe the trends, area of interest and emerging themes in these fields that could be analyzed qualitatively.

The findings show that the following drive themes emerged around education and research-driven sustainable architecture: emerging design as a core idea; concentration on the way of delivering and developing education; constructing and shaping the building; assessing current practices; acquiring and recruiting knowledge and new concepts; reporting on the current condition; portraying the target audience and ensuring the long-term viability of the architectural educational process. This insight provides academics and professionals a greater grasp of the state of the art, allowing them to direct their research toward developing concerns in education and research-driven sustainable design.

The study results provide an effective framework for collecting relevant content and identifying prominent topics in sustainable, passive and passive low-energy architecture for the creation of more sustainable urban and building environments. Furthermore, this qualitative and exploratory study may serve as a framework for those researching, creating and verifying different research approaches in education and research-driven sustainable architecture.

In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.

Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.

Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.

This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.