As most other EU countries, Hungary implemented severe lockdown measures during the pandemic, including the closure of the schools and childcare facilities. This meant that for several months a vastly increased volume of childcare had to be supplied by individual households without much institutional help. In the end of May 2020, we conducted a representative survey in Hungary to find out how the pandemic affected the gendered division of these childcare duties. We found that on average, in relative terms, men have increased their contributions at roughly the same rate (by 35 percent) as women. But given that women had been doing a lot more childcare work before the pandemic, in absolute terms, women's contributions grew significantly more than men's and the gap between men and women has increased in absolute work hour terms. This was particularly so among a specific group of women: middle class, highly educated citydwellers. Our data suggest that in Hungary the pandemic increased gender inequality the most among the highest educated.

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In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.

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The paper discusses explanations for attitudes towards immigrants before and after the start of the 'migrant crisis'. Though the crisis caused changes in peoples' attitudes all over Europe, the Hungarian case is special due to the Hungarian government's intensive antiimmigration campaign. To explain the circumstances people encountered during the crisis and the campaign, we first prove that moral panic abounded in society. Then, we show the background effects which affected the emergence of attitudes towards immigration in a political context. In the second part of the paper, we introduce a path model to explain the presumed effect of migration. We analyze this model with regard to the different political party preference groups, assuming that the government's anti-immigration campaign affected people's opinions and that people with different party preferences had different attitudes towards immigration: namely, those who were sympathetic to the incumbent party had more negative attitudes towards immigration. This effect has two interpretations. The first is that those who sympathized with the incumbent party were more sensitive to its messages. The other is that those who resonated more with the campaign changed their party preference to favour Fidesz, but those who resonated less with the campaign but used to be sympathizers of Fidesz do not support them anymore. The models show that before the migrant crisis there were only slight differences between political preference groups regarding how anti-migrant attitudes arose. However, after the start of the crisis (and the campaign), diverse processes could be identified in the different political groups, especially in the case of Fidesz sympathizers.

Many countries have secured larger quantities of COVID-19 vaccines than their population is willing to take. The abundance and the large variety of vaccines created not only an unprecedented intensity of vaccine related public discourse, but also a historical moment to understand vaccine hesitancy better. Yet, the heterogeneity of hesitancy by vaccine types has been neglected in the existing literature so far. We address this problem by analysing the acceptance and the assessment of five vaccine types. We use information collected with a nationally representative survey at the end of the third wave of the COVID-19 pandemic in Hungary. During the vaccination campaign, individuals could reject the assigned vaccine to wait for a more preferred alternative that enables us to quantify revealed preferences across vaccine types. We find that hesitancy is heterogenous by vaccine types and is driven by individuals’ trusted source of information. Believers of conspiracy theories are more likely to evaluate the mRNA vaccines (Pfizer and Moderna) unacceptable. Those who follow the advice of politicians are more likely to evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable. We argue that the greater selection of available vaccine types and the free choice of the individual are desirable conditions to increase the vaccination rate in societies.

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There are still many sociologists who are skeptical of the findings of big data-based analysis of social-data, questioning the potential of this knowledge production and its contribution to the scientific discourse of sociology.The chapter shows that this tension can be addressed through the redefinition of the research methodological basis of sociology, by the organic incorporation of data science know-how into its methods; the combined application of qualitative and quantitative analysis; and, the use of knowledge-driven science instead of the data-driven approach.The theoretical, methodological, and topical pathways between traditional and computational sociology emerge gradually along the chapter, which also includes plenty of illustrative examples of research situated at the interplay between sociology and data science. As our overview shows, there are new possibilities for sociological research, which are, in some sense, just by-products of information science. We introduce recently developed methods, which can be applied to specific sociological problems outside the scope of business applications. We present sociological topics not yet studied in this area and show new insights the approach can offer to classical sociological questions. As our aim is to encourage sociologists to enter this field, we discuss the new methods on the base of the classic quantitative approach, using its concepts and terminology and addressing the question of how traditionally trained sociologists can acquire new skills.