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Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches.
In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some potential solutions to the concerns and provide meaningful and diverse examples of its sociological application, most of which are related to research on Eastern European societies. The focus will be on the use of NLP in quantitative text analysis. Solutions are provided concerning how sociological knowledge can be incorporated into the new methods and how the new analytical tools can be evaluated against the principles of traditional quantitative methodology.

In this article, we argue that offline inequalities, such as core–periphery relations of the music industry, are reproduced by streaming platforms. First, we offer an overview of the reproduction of inequalities and core–periphery dynamics in the music industry. Then we illustrate this through a small-scale network analysis case study of Hungarian metal bands' connections on Spotify. We show that the primary determinant of a given band's international connectedness in Spotify's algorithmic ecosystem is their international label connections. Bands on international labels have more reciprocal international connections and are more likely to be recommended based on actual genre similarity. However, bands signed with local labels or self-published tend to have domestic connections and to be paired with other artists by Spotify's recommendation system according to their country of origin.

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 anti-immigration 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.

We contribute to faultline research by identifying familiarity and cross-subgroup communication as potential moderators in the relationship between diversity faultline and team performance. We employ a novel experimental design utilizing escape rooms as a noninterventional social laboratory, enabling us to capture real-time interactions among 40 teams engaged in problem-solving activities. We find that team familiarity has a negative influence and a suppression effect on success. Faultline affects team success negatively when faultline-induced subgroups do not communicate enough with each other. Our work contributes to a better understanding of complex processes and interdependencies that lead to team success or failure.

We analysed determinants of willingness to help using a factorial survey approach (n=405 individuals, n=5937 vignettes). We tested the effect of situational characteristics and how characteristics of the bystander and the person in need influence willingness to help in hypothetical situations within the framework of a cost-reward model. We found that the situation itself has the strongest effect: willingness to help was strongest when the net gain of helping was positive, whereas it was weaker in situations when the cost of helping and cost of not receiving help were equal. These results provide support for the cost-reward model of helping and illustrate that the factorial survey approach could supplement or in some cases replace more widely used experimental methods (laboratory or field experiments).

Many countries have secured larger quantities of COVID-19 vaccines than their populace is willing to take. This abundance and variety of vaccines created a historical moment to understand vaccine hesitancy better. Never before were more types of vaccines available for an illness and the intensity of vaccine-related public discourse is unprecedented. Yet, the heterogeneity of hesitancy by vaccine types in certain segments of society has been neglected so far, even though factual or believed vaccine characteristics and patient attributes are known to influence acceptance. In this paper, we address this problem by analysing acceptance and assessment of five vaccine types using information collected with a nationally representative survey (N=1000) at the end of the third wave of the COVID-19 pandemic in Hungary, where a unique portfolio of vaccines were available to the public in large quantities. Our special case enables us to quantify revealed preferences across vaccine types since one could evaluate a vaccine unacceptable and even could reject an assigned vaccine to wait for another type. We find that the source of information that respondents trust characterizes their attitudes towards vaccine types differently and leads to divergent vaccine hesitancy. Believers of conspiracy theories were significantly more likely to evaluate the mRNA vaccines (Pfizer and Moderna) unacceptable while those who follow the advice of politicians evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable with higher likelihood. We illustrate that the rejection of non-desired and re-selection of preferred vaccines fragments the population by the mRNA versus other type of vaccines while it generally improves the assessment of the received vaccine. These results highlight that greater variance of available vaccine types and individual free choice are desirable conditions that can widen the acceptance of vaccines in societies.

The analysis of social discourses from the perspective of historical changes deserves special attention. Such a study could play a key role in revealing social changes and latent narrative of those in power; and understanding the underlying social dynamic in a given period. Until the recent years, such issues were analyzed mainly in a qualitative approach. In our paper we present a new way of revealing/discovering and interpreting social discourses using an advanced NLP method called word embedding. Based on word similarities we can understand the main structural frames of a given system and using a dynamic approach we can reveal the social changes in a historical period. In our study we created a large corpus from the Hungarian "Pártélet" journal (1956–89). This was the official journal of the governing party, hence it represents not just a media discourse of the era, but the official discourse of the government, too. One of the main focal points of our research is to study the evolution of the semantic content of some of the concepts related to the topics of agriculture and industry, which are two central notions of the examined era.

The analysis of social discourses from the perspective of historical changes deserves special attention. Such a study could play a key role in revealing social changes and latent narrative of those in power; and understanding the underlying social dynamic in a given period. Until the recent years, such issues were analyzed mainly in a qualitative approach. In our paper we present a new way of revealing/discovering and interpreting social discourses using an advanced NLP method called word embedding. Based on word similarities we can understand the main structural frames of a given system and using a dynamic approach we can reveal the social changes in a historical period. In our study we created a large corpus from the Hungarian "Pártélet" journal (1956–89). This was the official journal of the governing party, hence it represents not just a media discourse of the era, but the official discourse of the government, too. One of the main focal points of our research is to study the evolution of the semantic content of some of the concepts related to the topics of agriculture and industry, which are two central notions of the examined era.