The pandemic of the coronavirus disease 2019 (COVID-19), has gained extensive coverage in public media and global news, generated international and national communication campaigns to educate the communities worldwide and raised the attention of everyone. The coronavirus has caused viral pneumonia in tens of thousands of people around the world, and the COVID-19 outbreak changed most countries’ routines and concerns and transformed social behaviour. This study explores the potential use of Google Trends (GT) in monitoring interest in the COVID-19 outbreak and, specifically, in personal protective equipment and hand hygiene, since these have been promoted by official health care bodies as two of the most protective measures. GT was chosen as a source of reverse engineering data, given the interest in the topic and the novelty of the research. Current data on COVID-19 are retrieved from GT using keywords in two languages—Portuguese and Polish. The geographical settings for GT are two countries: Poland and Portugal. The period under analysis is 20 January 2020, when the first cases outside China were known, to 15 June 2020. The results show that there is a correlation between the spread of COVID-19 and the search for personal protective equipment and hand hygiene and that GT can help, to a certain extent, understand people’s concerns, behaviour and reactions to sanitary problems and protection recommendations.

This study examines the possibility of correlation between the data on human mobility restrictions and the COVID-19 infection rates in two European countries: Poland and Portugal. The aim of this study is to verify the correlation and causation between mobility changes and the infection spread as well as to investigate the impact of the introduced restrictions on changes in human mobility. The data were obtained from Google Community Mobility Reports, Apple Mobility Trends Reports, and The Humanitarian Data Exchange along with other reports published online. All the data were organized in one dataset, and three groups of variables were distinguished: restrictions, mobility, and intensity of the disease. The causal-comparative research design method is used for this study. The results show that in both countries the state restrictions reduced human mobility, with the strongest impact in places related to retail and recreation, grocery, pharmacy, and transit stations. At the same time, the data show that the increase in restrictions had strong positive correlation with stays in residential places both in Poland and Portugal.

LCM Journal, edited by the Department of Language Mediation and Intercultural Communication of Università degli Studi di Milano, aims to investigate the linguistic and cultural phenomena characterising the contemporary world.

In past years, translation education has shifted from a “transmissionist approach” to the unchallenged use of collaborative learning, with extensive recourse to teamwork and Project-based Learning. Students are encouraged to develop their translation and interpersonal skills in collaborative environments, focusing on translation as a process. Throughout this process, mistakes are valued as learning opportunities, and no translated text is seen as an authoritative version to which all others are unfavourably compared. This approach is somewhat at odds with an increasingly competitive job market, in which translators must be capable of autonomous and individual work, and translation is mainly viewed and evaluated as a final product. Although translation workflows involve a growing amount of group effort, translators still need to work alone and take responsibility for their own versions and translation choices. In order to prepare students to become professional translators, the annual Technical and Scientific Translation Award aims not only to give winners visibility before potential employers but also to work as an opportunity for young translation students and graduates to put their skills to the test. This paper focuses on the translation competences activated by the contest, as defined by the Competence Framework produced by the European Masters in Translation Network. It also discusses the potential of this initiative as a motivational tool for translation students and graduates, concluding that individual contests counterbalance the predominance of collaborative activities in the classroom and are therefore a relevant complement to academic training.

The accelerating digital transformation of the insurance sector intensified competitive pressure, and the expanding use of data science technologies have substantially reshaped marketing strategy formation, creating new opportunities for more precise customer segmentation, predictive analytics, and strategic optimization in insurance companies. The aim of this paper is to examine how data science techniques can be operationalized to support marketing strategy formation in the Ukrainian insurance market, which is characterized by high uncertainty and structural transformation. This study addresses three research questions: which customer attributes are the most significant determinants of insurance product selection in the Ukrainian insurance market; to what extent can decision tree models provide accurate and interpretable segmentation for insurance marketing purposes; and how can the results of data science models be translated into actionable insights for marketing strategy implementation by insurers and practitioners? Using anonymized customer relationship management (CRM) data from a top-10 Ukrainian insurer (N = 14,000 clients), a decision tree classification model was developed to identify key determinants of insurance product choice. Model performance was evaluated using accuracy metrics and rule support. A qualitative description was employed to support audience interpretation. The results indicate that customer loyalty (measured by the number of active contracts), geographic location, and age are the most significant predictors of insurance product selection, with an overall classification accuracy of 93.5%. The extracted decision rules provide transparent and interpretable insights that can be directly translated into targeted marketing actions. The findings demonstrate how interpretable machine learning models can support data-driven targeting, improve marketing return on investment (ROI), and enhance personalization in insurance services. The study highlights key areas of data science application in insurance marketing, including customer segmentation, targeted marketing, and campaign optimization. This study contributes empirical evidence from an under researched, high-uncertainty market and proposes a structured, interpretable analytics approach based on classical machine learning for insurance marketing strategy development. It bridges the gap between analytical modelling and practical marketing strategy, offering insights that are directly applicable to insurance practitioners