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Since 2020, the world has faced two unprecedented shocks: lockdowns (regulation) and the invasion of Ukraine (war). Although we realise the health and economic effects of these shocks, more research is needed on the effect on happiness and whether the type of shock plays a role. Therefore, in this paper, we determine whether these macro-level shocks affected happiness, how these effects differ, and how long it takes for happiness to adapt to previous levels. The latter will allow us to test whether adaptation theory holds at the macro level. We use a unique dataset of ten countries spanning the Northern and Southern hemispheres derived from tweets extracted in real-time per country. Applying Natural Language Processing, we obtain these tweets' underlying sentiment scores, after which we calculate a happiness score (Gross National Happiness) and derive daily time series data. Our Twitter dataset is combined with Oxford's COVID-19 Government Response Tracker data. Considering the results of the Difference-in-Differences and event studies jointly, we are confident that the shocks led to lower happiness levels, both with the lockdown and the invasion shock. We find that the effect size is significant and that the lockdown shock had a bigger effect than the invasion. Considering both types of shocks, the adaptation to previous happiness levels occurred within two to three weeks. Following our findings of similar behaviour in happiness to both types of shocks, the question of whether other types of shocks will have similar effects is posited. Regardless of the length of the adaptation period, understanding the effects of macro-level shocks on happiness is essential for policymakers, as happiness has a spillover effect on other variables such as production, safety and trust. [ABSTRACT FROM AUTHOR]

Maritime transport plays a key role in the global and local economy, accounting for 80% of global trade by volume. This makes smooth operation of the maritime transport essential. However, the sector faces the constant risk of various crises and their potential consequences that may significantly impact and disrupt the movement of goods on local, regional, and global levels. In recent years, two notable crises, namely the COVID-19 pandemic and the war in Ukraine, have been observed. This paper aims to analyse how international crises, such as armed conflicts and pandemics, influence maritime traffic and assess their impact on both global and local economies. A comparison is drawn between the periods before and during the COVID-19 pandemic and before and during the war in Ukraine to exemplify the effects of crises. The findings are then extrapolated to apply to potential future crises. Vessel movements are studied using data collected from Automatic Identification Systems (AIS). In our quantitative approach, we analyse big data using dedicated tools and visualisation techniques to gain insights into specific phenomena. The paper identifies economically significant regions for maritime traffic and examines the impact of crises on their performance. Its unique value lies in its flow-based analysis of changes in maritime traffic. The main conclusion is that China's importance for worldwide maritime traffic is increasing. This makes the global economy heavily reliant on China to a substantially greater extent than it is, for example, on Russia. Consequently, any crisis in the China region could exert a dramatic impact on the global economy. The paper also discusses observations of changes in maritime traffic following the outbreak of the war in Ukraine. [ABSTRACT FROM AUTHOR]

The impact of the war in Ukraine and migration has affected the ecommerce markets of the recipient countries, presenting both opportunities, in the form of an increased consumer base, and challenges, such as the lack of a clear development vision. This paper aims to investigate the influence of migration processes on the development of e-commerce in Poland and examine the feasibility of using forecasting methods by e-commerce companies under these conditions for future activity planning. To fulfill the research objective, the following tasks were addressed: investigating the current state of e-commerce development influenced by migration processes; exploring modern migration processes and their impact on global economies; assessing the impact of migration from Ukraine on the Polish market; and analyzing a Polish online store to develop a model for forecasting data and planning activities under the influence of migration processes. To achieve this goal, three models were constructed: a multiple regression model to assess the level of migration processes' influence on e-commerce; a neural network to forecast sales for a Polish e-commerce store; and cluster analysis to identify clusters of goods most affected by migration processes. The study analyzed the nuances of modern migration processes and assessed the reverse effect of migration as a driver of e-commerce development. Migration stimulates e-commerce by altering consumer behavior and logistics routes, increasing exports and imports, and fostering the spread of digital entrepreneurship. Using data from a Polish online store, the study modeled the impact of market changes on the company's operations and identified the most significant factors. Thus, the analysis explored the impact of migration on ebusiness in Poland through constructed models. Regression analysis revealed that migration processes have contributed to the development of the Polish online store's sales, thanks to the increase in migrant consumers and rising price levels. A neural network was developed with machine learning, incorporating macroeconomic and demographic factors into its forecasting typology. Cluster analysis was employed to examine the online store's assortment, identifying clusters by sales volume and migrants' influence. The analysis determined that, following the onset of the migration movement, categories experiencing a surge in demand from refugees, such as baby food products, appliances, telephones, furniture, and communication devices, saw the most significant growth. [ABSTRACT FROM AUTHOR]

Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of tweets related to Ebola, E-Coli, Dengue, Human Papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both of these viruses. None of the prior works in this field analyzed tweets focusing on both COVID-19 and MPox simultaneously. To address this research gap, a total of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted between 7 May 2022 and 3 March 2023, were studied. The findings and contributions of this study are manifold. First, the results of sentiment analysis using the VADER (Valence Aware Dictionary for sEntiment Reasoning) approach shows that nearly half the tweets (46.88%) had a negative sentiment. It was followed by tweets that had a positive sentiment (31.97%) and tweets that had a neutral sentiment (21.14%), respectively. Second, this paper presents the top 50 hashtags used in these tweets. Third, it presents the top 100 most frequently used words in these tweets after performing tokenization, removal of stopwords, and word frequency analysis. The findings indicate that tweets in this context included a high level of interest regarding COVID-19, MPox and other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that compares the contributions of this paper with 49 prior works in this field is presented to further uphold the relevance and novelty of this work. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyze the market response of the aerospace and defense industry and the airline industry to the ongoing conflict between Ukraine and Russia based on the sentiments from war-related news articles over the period from October 2021 to June 2022. Design/methodology/approach: The study uses the news article database of Global Database of Events, Languages and Tone (GDELT) to create a new set of variables that reflect the news sentiment regarding war and conflict. By investigating the newly created sentiment variables in combination with traditional event study methodology, the authors seek to find out whether sentiment indicators can be helpful to rationalize the evolution of the different stock markets before and after the conflict. Findings: The authors' results point out a significant negative impact of the war on the airline market and a positive impact on the defense market. The authors' study also introduces a new set of war-related news-based sentiment variables that is significant to explain the evolution of the two markets before and after the war. The relationships between this study's new set of variables and the performance of the two markets are also proven to be significantly impacted by the invasion. Originality/value: To the best of the authors' knowledge, this is the first research to use the news sentiment related to the topic of war and conflict to explain the market movement of different industries during the Ukraine invasion. [ABSTRACT FROM AUTHOR]

This paper presents a review of Big Data sources that could be helpful in determining, estimating, and forecasting the forced emigration flows of refugees from Ukraine. The text shows how a Big Data approach can help assess refugees' intentions. Using insights from social-media platforms such as Facebook, Instagram, and YouTube is useful, because data here are available faster than any official data in the refugee crisis triggered by the Russian attack on Ukraine on 24 February 2022. [ABSTRACT FROM AUTHOR]

This article utilizes a unique data set to examine the relationship between a group of potential explanatory variables and educational corruption in Ukraine. Our corruption controls include bribing on exams, on term papers, for credit, and for university admission. We use a robust nonparametric approach in order to estimate the probability of bribing across the four different categories. This approach is shown to be robust to a variety of different types of endogeneity often encountered under commonly assumed parametric specifications. Our main findings indicate that corruption perceptions, past bribing behavior, and the perceived criminality of bribery are significant factors for all four categories of bribery. From a policy perspective, we argue that when bribe control enforcement is difficult, anti-corruption education programs targeting social perceptions of corruption could be appropriate. ( JEL K42, J16, C14) [ABSTRACT FROM AUTHOR]

Digitalization processes bring about radical transformations in the content and nature of work, leading to shifts in the demand for certain skills and abilities and the emergence of new occupations. Traditional survey-based sources of information about the labour market prove insufficient to track these changes for employment policy purposes. In this article, the authors reveal alternative data sources on the labour market, made possible due to the development of new digital technologies, and delineate their respective advantages and drawbacks. Additionally, the authors systematize international experiences in leveraging digital technologies and Big Data for statistical and information-analytical research on the labour market. Having analysed the leading research-analytical projects in the USA, Great Britain, and EU countries, the authors found them to be focused on constructing functional intellectual and analytical systems for the labour market. These projects aim to develop methodologies and promote widespread adoption of digital tools for working with Big Data, significantly expanding the capabilities of labour market information and analytical systems. The authors examined specific projects that used Big Data from online job vacancies to assess the demand and supply of jobs, as well as to analyse and forecast the requirements for skills and competences that would be feasible for adoption in Ukraine. Big Data from specialized online portals, servers and services were found to serve as powerful resources to supplement and enrich the existing conventional system of labour market statistics and analytics. Finally, the authors substantiate the need and expediency of creating a fundamentally new Labour Market Information and Analytical System (LMIAS) in Ukraine and coordinating it with labour market platforms in EU countries. They also identify a range of problematic issues that require in-depth research and resolution in Ukraine, such as ensuring the representativeness of online job vacancy data, improving the classifiers of occupations, abilities and skills, and introducing methods for integrating statistical, administrative, and Big Data on the labour market. [ABSTRACT FROM AUTHOR]

The 2022 Russian invasion of Ukraine sent shockwaves through Europe and led to rapid policy changes concomitant with variations in citizen perceptions. This article analyses how EU public opinion on security and defence matters has reacted to the war: what patterns of change and continuity can be detected, what differences are visible between Member States, and how might those be explained? Our analysis draws on big data-based sentiment analysis of news sources, reflecting a widely recognized connection between media coverage and public opinion – especially during crisis times – and complementing more traditional measurements of citizen perceptions such as opinion polls. Broadly speaking, we find that the invasion has heightened rather than fundamentally altered underlying trends. Our article contributes to a growing literature on the acceptability of European integration in security and defence, showing that publics are generally supportive of it, and regard it as complementary to NATO. [ABSTRACT FROM AUTHOR]

The purpose of this study is to contribute to the maintenance and compilation of the consumer price index (CPI) in the current extreme situation caused by the Russian military aggression against Ukraine. In these extreme conditions, official statistics is faced with the task of maintaining the regularity, completeness and quality of the production of statistical information, including the CPI, which is one of the key economic indicators. The interaction between the ideal and conditional concepts of the index and their practical implementation is considered as a potential source of compilation improvement. The author argues that the main factor of the modern criticism of the CPI is the systematic deviation of the practical form of the index from its theoretical foundations. One way to solve this problem is to use new sources of information, especially big data cash registers. In today's extreme conditions, cash data can extensively address the issue of limited and untimely access to primary data sources needed to compile the CPI, as well as promptly take into account the changes in consumption patterns caused by significant migratory flows from the dangerous areas, and changes in the supply offer due to the rupture of supply chains. [ABSTRACT FROM AUTHOR]

The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based on well-known and commonly used clinimetric scales (e.g., in psychiatry, psychology and physiotherapy). The simultaneous use of multiple scales, and the complexity of describing the quality of life with them, require complex fuzzy computational solutions. The aim of the study is twofold: (1) To develop a fuzzy model that allows for the detection of changes in life satisfaction scores (data on the influence of the COVID-19 pandemic and the war in the neighboring country were used). (2) To develop more detailed guidelines than the existing ones for further similar research on more advanced intelligent systems with computational models which allow for sensing, detecting and evaluating the psychical state. We are concerned with developing practical solutions with higher scientific and clinical utility for both small datasets and big data to use in remote patient monitoring. Two exemplary groups of specialists at risk of occupational burnout were assessed three times at different intervals in terms of life satisfaction. The aforementioned assessment was made on Polish citizens because the specific data could be gathered: before and during the pandemic and during the war in Ukraine (a neighboring country). That has a higher potential for presenting a better analysis and reflection on the practical application of the model. A research group (physiotherapists, n = 20) and a reference group (IT professionals, n = 20) participated in the study. Four clinimetric scales were used for assessment: the Perceived Stress Scale (PSS10), the Maslach Burnout Scale (MBI), the Satisfaction with Life Scale (SWLS), and the Nordic Musculoskeletal Questionnaire (NMQ). The assessment was complemented by statistical analyses and fuzzy models based on a hierarchical fuzzy system. Although several models for understanding changes in life satisfaction scores have been previously investigated, the novelty of this study lies in the use of data from three consecutive time points for the same individuals and the way they are analyzed, based on fuzzy logic. In addition, the new hierarchical structure of the model used in the study provides flexibility and transparency in the process of remotely monitoring changes in people's mental well-being and a quick response to observed changes. The aforementioned computational approach was used for the first time. [ABSTRACT FROM AUTHOR]

From the outset of the war in Ukraine, extensive crises in many sectors of the world economy have occurred, with firms offering services and products both online and through physical stores facing serious problems. These problems are mainly related to higher operational costs and the lack of website visibility. For this research study, centralized payment network organizations (CPNs), firms providing online payment services through their networks, were selected and analytical data from their websites were collected for a period of 6 months. The main focus of this research study is to evaluate benefits and the role of social media strategies for CPNs' digital marketing performance during crisis events and to also assess their utility as a risk-management tool. Following data collection, the authors performed statistical processes (regression and correlation analysis) and stationary modeling with Fuzzy Cognitive Mapping (FCM) tools; finally, dynamic simulations were performed by utilizing Agent-Based Models (ABM). The authors suggest that various variables of CPNs' social media platforms can aid in improving their digital marketing performance and, using proper analysis, can lead to higher user social engagement, thus rendering social media strategy a useful risk-management tool. [ABSTRACT FROM AUTHOR]