What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the "Digital Service Usage Satisfaction Model"

Emotional straying: Flux and management of women's emotions in social media

In recent years, social media, which has emerged with the core focus on interaction within "acquaintance networks," has gradually been infiltrated by "strangers," leading to the disorientation of many users, especially women, amidst the diverse and intricate social platforms and emotional landscapes. Grounded in the experiential perspective of social media users, this study explores the correlations among woman emotions, satisfaction, and behavior, starting from the standpoint of the impact of social media. Through in-depth interviews with woman cohorts in China, various dimensions such as emotional fluctuations, satisfaction levels, and behaviors in social media were examined. The findings reveal that emotional expression serves as a primary motivation and purpose for users to sustain their engagement with social media. Additionally, emotional masking represents a proactive operational behavior induced by the needs for social relationship maintenance and the accumulation of social capital. Furthermore, emotional management manifests as user-initiated abandonment or shift of social media activities in response to perceived emotional stress. On this basis, a conceptual model integrating woman emotions, satisfaction, and behavior in the context of social media was constructed. The outcomes of this research hold significant theoretical and practical implications for future studies on woman emotions and behaviors, as well as for the development of social media functionalities, content management, public media usage, and psychological health interventions.

A systematic review of the use of topic models for short text social media analysis

Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-023-10471-x.

A novel multi-attribute decision-making for ranking mobile payment services using online consumer reviews

The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.

Investigating the dark side of mobile bookkeeping applications: a moderated-mediation approach

Purpose

This study aims to examine the effect of usage, value, tradition, risk, compatibility and complexity barriers on user resistance to mobile bookkeeping applications. Furthermore, it also explores how the relationship between these barriers and user resistance is mediated by technostress. Finally, the authors analysed the moderating impact of self-efficacy on the mediating effect of technostress between barriers and user resistance.

Design/methodology/approach

Structured questionnaires were used to obtain data from 325 respondents. A structural equation modelling technique was used to investigate the hypotheses.

Findings

The findings suggest that usage, risk and tradition barrier has a significantly positive effect on user resistance intention. Also, results suggested that technostress plays an important role in framing customers’ resistance intention. Finally, the mediation effect of technostress between risk barrier and user resistance is higher for users having low levels of self-efficacy compared with users with high levels of self-efficacy.

Originality/value

The present research enriches the existing literature, especially in the field of mobile bookkeeping applications, user resistance, technostress and innovation resistance theory. It would help bookkeeping application developers design their apps, keeping the major user barriers in mind.

Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment

Artificial intelligence (AI) is expected to bring to the physical retail environment the kind of mass personalisation that is already common in online commerce, delivering offers that are targeted to each customer, and that adapt to changes in the customer's context. However, factors related to the in-store environment, the small screen where the offer is delivered, and privacy concerns, create uncertainty regarding how customers might react to highly personalised offers that are delivered to their smartphones while they are in a store. To investigate how customers exposed to this type of AI-enabled, personalised offer, perceive it and respond to it, we use the personalisation-privacy paradox lens. Case study data focused on UK based, female, fashion retail shoppers exposed to such offers reveal that they seek discounts on desired items and improvement of the in-store experience; they resent interruptions and generic offers; express a strong desire for autonomy; and attempt to control access to private information and to improve the recommendations that they receive. Our analysis also exposes contradictions in customers' expectations of personalisation that requires location tracking. We conclude by drawing an analogy to the popular Snakes and Ladders game, to illustrate the delicate balance between drivers and barriers to acceptance of AI-enabled, highly personalised offers delivered to customers' smartphones while they are in-store.

Self-promotion and online shaming during COVID-19: A toxic combination

A public shaming frenzy has spread through social media (SM) following the instigation of lockdown policies as a way to counter the spread of COVID-19. On SM, individuals shun the idea of self-promotion and shame others who do not follow the COVID-19 guidelines. When it comes to the crime of not taking a pandemic seriously, perhaps the ultimate penalty is online shaming. The study proposes the black swan theory from the human-computer interaction lens and examines the toxic combination of online shaming and self-promotion in SM to discern whether pointing the finger of blame is a productive way of changing rule-breaking behaviour. A quantitative methodology is applied to survey data, acquired from 375 respondents. The findings reveal that the adverse effect of online shaming results in self-destructive behaviour. Change in behaviour of individuals shamed online is higher for females over males and is higher for adults over middle-aged and older-aged.

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems.

Is this question going to be closed? Answering question closibility on Stack Exchange

Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this article is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score and area under the receiver operating characteristic curve (AUC) were above 0.90 irrespective of the topic of questions. By conceptualising question closibility, the article extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works.

Drivers of post-adoption of e-wallet among academics in Palestine: An extension of the expectation confirmation model

E-wallet is one of the latest innovations in the field of payments. However, despite numerous studies on the adoption of e-finance systems, the post-adoption phase is largely neglected. In this paper, we use the extended Expectation Confirmation Model (ECM) to address this gap by focusing on the study of consumers’ continuous intentions regarding the use of an e-wallet service. We conducted an electronic questionnaire-based survey among 503 e-wallet users in Palestine. Using structural equation modeling to analyze the conceptual model of the study, our results confirm that satisfaction, trust, and perceived usefulness have a significant impact on consumers’ continuous intention regarding e-wallet. In addition, the study found that perceived security has an insignificant impact on consumer satisfaction. The study has several implications: E-wallet providers should improve their services in terms of performance, privacy, and security to ensure customer loyalty in this competitive industry.

Managing buyer experience in a buyer-supplier relationship in MSMEs and SMEs

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers' expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer-supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer-supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer-supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers' technology capabilities (digital readiness, big data analytical capability).

What insisted baby boomers adopt unified payment interface as a payment mechanism?: an exploration of drivers of behavioral intention

Purpose

The unified payment interface (UPI) is in its early stages of adoption for baby boomers. This study explores the factors affecting the behavioral intention of baby boomers to adopt UPI. UTAUT was adopted as theoretical lens of the study and extended with ubiquity, privacy risk and perceived security. The impact of an external factor – effect of COVID-19 was also examined in this study.

Design/methodology/approach

A consumer intercept survey was used to collect data from baby boomers via a self-administered structured questionnaire. Structural equation modeling was used to establish the relationships among latent variables. Further, using bootstrap re-sampling technique, the role of perceived security as a mediator between risk, ubiquity and behavioral intention was examined.

Findings

The study confirmed that COVID-19 was the most influential external factor for baby boomers to adopt UPI, followed by performance expectancy, social influence, ubiquity, effort expectancy and perceived security. Apropos of UPI adoption by baby boomers, privacy risk negatively influenced perceived security, whereas perceived security fully mediated the relationship between risk, ubiquity and behavioral intention.

Research limitations/implications

The study focused only on baby boomers and their intention to adopt UPI. Hence the results cannot be generalized to all age groups and are specific to the cohort.

Originality/value

The present study aims to establish research findings on predicting antecedents of adopting a newly introduced payment mechanism and an exemplary Indian digital innovation, UPI, by baby boomers. This study is first to empirically explore intention of baby boomers toward adoption of UPI.

Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media.

The Research Trend of Security and Privacy in Digital Payment

The aim of this study is to synthesize the rapidly increasing literature on privacy and security risk of digital payment. By reviewing 591 studies, the literature on this topic was evaluated using a bibliographical approach to highlight the intellectual development of the field and recommend potential research directions in this still-emerging field. According to our assessment, academics have continued to focus on perceived privacy and security, while more multigroup analyses based on subdimensions of risk are needed. In addition, the vast majority of studies have not considered the inter-relationship between risk attributes. We analyse the potential causes of the lack of research diversity and provide additional suggestions to improve digital payment research in the future. This study will be valuable for academics, analysts, regulators, practitioners, and investors.

Public E-Participation Services as a Cure for Declining Voter Turnout

Purpose of this paper is to identify factors influencing the intention to use and develop a model for measuring the intention to use public e-participation services. As a added value, paper is examining the structure of needs for different levels of public e-participation services. As for the methodology, this paper provides an empirical evaluation of Davis's Technology Acceptance Model extended with non-technical constructs of the Planned Behavior Theory and Trust Model. Validity and hypotheses of the newly proposed multidimensional structural model were tested using Partial Least Squares Structural Equation Modeling. PLS-SEM research results significantly confirmed three out of seven hypotheses. There is a positive and statistically significant correlation between “Expected usefulness”, “Expected behaviour control” and “Trust in the Internet” with the intention to use public e-participation services (p<0.05). Concerning demand-side, research results demonstrate that the majority of the respondents prefers public e-participation services of a higher level of complexity.

Influencing Factors of Public Satisfaction with COVID-19 Prevention Services Based on Structural Equation Modeling (SEM): A Study of Nanjing, China

Service satisfaction with public policies is an important component of public service quality management, which is of great significance to the improvement of public service quality. Based on an online questionnaire survey and in combination with the characteristics of public policies and services, in this study the influencing factors of residents' satisfaction with COVID-19 pandemic prevention services were analyzed with structural equation modeling. The results reveal that the data fit the model well, and all the hypotheses formulated in this study were supported. Among the factors that were found to directly affect residents' satisfaction with pandemic prevention services, perceived quality (PQ) has the greatest impact on satisfaction, followed by the disaster situation (DS) and policy expectation (PE). The observed variables that have significant impacts on the latent variables were also explored. Regarding the main findings, the residents who were seriously affected by the pandemic tended to have lower satisfaction with the policies and services provided by the government. Moreover, the improvement of PQ was found to significantly increase pandemic prevention service satisfaction (SS). Finally, the residents with a good psychological status during the pandemic were found to have higher satisfaction. According to the results, implications for the prevention and control practices of similar public health emergencies are proposed.

How does entrepreneurial orientation and SDG orientation of CEOs evolve before and during a pandemic

Purpose

Aligning business innovation with the sustainable development goals (SDGs) creates immense opportunities to solve societal challenges along with business growth and productivity. This study aims to understand the evolution of Fortune firms' strategic addressing of SDG on social media as a step towards post-pandemic recovery. Using attribution theory as a theoretical lens, the authors try to investigate how entrepreneurial orientation (EO) and SDG orientation evolve with the crisis and affect the appreciation and advocacy of the SDG-related posts.

Design/methodology/approach

A mixed methodology of machine learning and Social media analytics such as content analysis, sentiment analysis and space–time analysis have been used, followed by multivariate analysis to validate the findings.

Findings

An evolution in CEOs’ strategic focus surrounding SDG dimensions was found, from economic in pre-pandemic phase to social and environment during the pandemic. The SDG disclosure on social media by the Fortune CEOs seems to have an influence on their social media reputation, whereas EO has no impact on social media reputation.

Research limitations/implications

Wise practice of EO in information diffusion by CEOs on social media may lead to a healthy relationship with the stakeholders and better firm performance. The SDG adoption at organisation level contributes towards a sustainable society and helps tackling the challenges faced during the pandemic.

Originality/value

This study analyses the contribution of the Fortune firms to achieve a sustainable society in a pandemic environment by strategic adoption of SDGs and effective use of digital platforms.

AI-enabled digital identity – inputs for stakeholders and policymakers

Purpose

This conceptual article’s primary aim is to identify the significant stakeholders of the digital identity system (DIS) and then highlight the impact of artificial intelligence (AI) on each of the identified stakeholders. It also recommends vital points that could be considered by policymakers while developing technology-related policies for effective DIS.

Design/methodology/approach

This article uses stakeholder methodology and design theory (DT) as a primary theoretical lens along with the innovation diffusion theory (IDT) as a sub-theory. This article is based on the analysis of existing literature that mainly comprises academic literature, official reports, white papers and publicly available domain experts’ interviews.

Findings

The study identified six significant stakeholders, i.e. government, citizens, infrastructure providers, identity providers (IdP), judiciary and relying parties (RPs) of the DIS from the secondary data. Also, the role of IdP becomes insignificant in the context of AI-enabled digital identity systems (AIeDIS). The findings depict that AIeDIS can positively impact the DIS stakeholders by solving a range of problems such as identity theft, unauthorised access and credential misuse, and will also open a possibility of new ways to empower all the stakeholders.

Research limitations/implications

The study is based on secondary data and has considered DIS stakeholders from a generic perspective. Incorporating expert opinion and empirical validation of the hypothesis could derive more specific and context-aware insights.

Practical implications

The study could facilitate stakeholders to enrich further their understanding and significance of developing sustainable and future-ready DIS by highlighting the impact of AI on the digital identity ecosystem.

Originality/value

To the best of the authors’ knowledge, this article is the first of its kind that has used stakeholder theory, DT and IDT to explain the design and developmental phenomenon of AIeDIS. A list of six significant stakeholders of DIS, i.e. government, citizens, infrastructure providers, IdP, judiciary and RP, is identified through comprehensive literature analysis.

Users’ Evaluation of a New Web Browser Payment Interface for Facilitating the Use of Multiple Payment Systems

The availability of multiple (mobile) electronic payment systems ((M)EPS) has led to the development of web browser payment interfaces that support various payment systems, facilitate the transaction, the choice of the payment system, and perform the payment. However, so far, no in-depth study on user satisfaction determinants with these interfaces has been conducted. Our work aims to cope with this issue. Thus, based on the analysis of payment literature and Google Chrome web browser (GCWB) payment interface, we propose a new web browser payment interface that considers users’ preferences to support multiple payment systems. Furthermore, we have developed a theoretical model to determine users’ preferences to support multiple payment systems. Our model is based on the extension of technology acceptance models. Finally, we evaluated both the theoretical and proposed payment interface through a survey research approach (n = 266); data were collected, and the hypotheses were tested via statistical analysis (chi-square test, regression coefficients). Our experimental results revealed that our proposed interface is accepted, easy to use, and satisfies users’ needs. The key factors for accepting a new web browser payment interface are ease of use, usefulness, security, confidentiality, privacy, payment method preferences, visual interface design, and credibility.

The Study of Factors on the Small and Medium Enterprises' Adoption of Mobile Payment: Implications for the COVID-19 Era

The coronavirus disease 2019 (COVID-19) pandemic pushes people looking for shopping alternatives, seeking to avoid handling cash in favor of a safe and quick mobile payment. At this juncture, this paper examines the determinants of the adoption of mobile payment services among small and medium enterprises (SMEs) in China. The study proposes four-dimensional factors (business factors, technological competence, environment, and consumers' intentions) based on the literature review findings to understand the challenges of adopting mobile payment. A questionnaire is designed to solicit information from the participants. The findings reveal that business factors, technological competencies of SMEs in China, and the environment positively influence mobile payment adoption. Consumer intention has almost no influence on the adoption of mobile payment. Potential implications for the COVID-19 era are also discussed.

Exploring engagement among mobile app developers – Insights from mining big data in user generated content

Purpose

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

Design/methodology/approach

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Research limitations/implications

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

Practical implications

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

Social implications

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

Originality/value

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling

The COVID-19 pandemic has had a destructive effect on the tourism sector, especially on tourists’ fears and risk perceptions, and is likely to have a lasting impact on their intention to travel. Governments and businesses worldwide looking to revive and revamp their tourism sector, therefore, must first develop a critical understanding of tourist concerns starting from the dreaming/planning phase to booking, travel, stay, and experiencing. This formed the motivation of this study, which empirically examines the tourist sentiments and concerns across the tourism supply chain. Natural Language Processing (NLP) using sentiment analysis and Latent Dirichlet Allocation (LDA) approach was applied to analyze the semi-structured survey data collected from 72 respondents. Practitioners and policymakers could use the study findings to enable various support mechanisms for restoring tourist confidence and help them adjust to the’new normal.’

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic

The article examines how many leaders used Twitter during the COVID-19 pandemic, in what way, and the impact they had on the public. In the context of Twitter, the impact on the public refers to the growth in followers as it signifies the increased interest of the public about information. 50,872 tweets were collected from 143 state leaders and an original dataset was created containing information on the growth of followers. Ordinary least squares regression models were used for the analysis. It was found that 64.8% of UN member states had a leader that tweeted about COVID-19. Furthermore, a significant increase in the number of followers during the pandemic compared to months prior was noted. Since March, the pandemic has been a dominant topic on Twitter. During the COVID-19 pandemic, the highest percentage increase in gaining Twitter followers was experienced by politicians who frequently tweeted and those who had a lower ratio of the number of followers to internet users. The research implies that citizens are interested in being informed about emergencies through social networks, and government officials should use them.