Data Science in Social Research

One of the challenges of applying data analytics in sociology is the institutionalization of data science outside of sociology, as the former expertise of sociology was based on its own method of research. Another challenge is epistemological in nature, relates to the noisiness and validity of digital data, and the question of explanation/causation, which is highly important for sociology. These challenges give the background of the tension between the Big Data based, social-related findings and the sociological skepticism questioning the potential of this knowledge-production. The challenges can be solved through the redefinition of the research methodological basis of sociology, by the organic incorporation of data science know-how to its own methods. The solution also needs the combined application of qualitative and quantitative analysis motives, and the use of knowledge-driven science instead of the data-driven approach.

Foregoing results

Our research stream is motivated by a continuously growing social science interest in data science. As an example, see the case of automated text analytics: the following figure shows that the popularity of automated text analytics has been continuously growing in recent years in general and also in each discipline investigated (to access publication data we used Dimensions, https://dimensions.ai). Each trend line is growing persistently even after normalizing for the total number of publications in the discipline. The topic’s percentage portion in sociology increased faster than in sciences in general. In summary, automated text analytics is becoming an increasingly recognized approach in sociology.

Related Results

Koltai, Júlia – Kmetty, Zoltán – Bozsonyi, Károly (2019) From Durkheim to machine learning – finding the relevant sociological content in a social media discourse. In: Rudas, Tamás – Péli, Gábor (eds.) Pathways Between Social Science and Computational Social Science – Therories, Methods and Interpretations. New York, NY, Springer. (forthcoming)

2019.12.15. Publication

The phenomenon of suicide is in the focus of social scientists since Durkheim. Internet and social media sites provide new ways for people to express their positive feelings, but they are also platforms to express suicide ideation or depressed thoughts. Most of these contents are not notes about real suicides, [...]

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Németh, Renáta; Koltai, Júlia (2019): Sociological knowledge discovery through text analytics. In: Rudas, Tamás – Péli, Gábor (eds.) Pathways Between Social Science and Computational Social Science – Therories, Methods and Interpretations. New York, NY, Springer. (forthcoming) 

2019.12.01. Publication

In our work, based on recent research reports, we discuss the advances, challenges and opportunities of Big Data text analytics in sociology. The advances include the utilization of the originally and primarily business and technology-oriented development of information technology, data science, AI and NLP; and also, the rapid growth of [...]

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Previous publications in epistemology/sociology of science

2019.06.26. Publication

Katona, Eszter, Németh, Renáta, Kmetty, Zoltán: Text analytics in social sciences – An example for NLP’s application (in Hung., submitted) Bárdits, Anna, Németh, Renáta (2017): The rite of statistical significance testing – contemporary critics; the rite in sociology. Szociológiai Szemle, 27:(1) pp. 119-125. (in Hung.) Bárdits, Anna, Németh, Renáta, Terplán, [...]

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Kmetty, Zoltán – Koltai, Júlia: Understanding Cultural Choices with NLP (2019). Presentation at the Data Science Meetup Budapest, May 9, 2019.

2019.05.09. Presentation

Parallel with the rise of digital textual data, natural language processing methods developed rapidly in the last decade. In our presentation, we will focus on artificial neural network based word embedding methods, which became widespread in recent years. Different fields apply these methods, such as linguists for dictionary building; developers [...]

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Bartus, Tamás – Kisfalusi, Dorottya – Koltai, Júlia (2019) Logisztikus regressziós együtthatók összehasonlítása (The Comparison of Coefficients in Logistic Regression) In: Statisztikai Szemle (Hungarian Statistical Review) 97(3): 221-240.

2019.03.09. Publication

Recently, increasing attention has been devoted to the problem that estimated coefficients of logistic (and other non-linear) regression models cannot be compared across groups, samples, or nested model specifications due to the possible differences in the magnitude of unobserved heterogeneity. This study reviews methods which aim to solve this problem [...]

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Kmetty, Zoltán – Koltai, Júlia: Big data based decision making mechanisms from the viewpoint of social sciences. Presentation at the event of HUB Design House, called  ‘The Power of Big Data’ January 9, 2019.

2019.01.09. Presentation

In our presentation, we presented the possibilities of large scale data-based decision making, focusing on the dangers, when this type of decision making does not work properly. Within this latter topic, we emphasised the importance of interpretation and causality.

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Németh, Renáta: Data Science and Statistics. Presentation and opening of a debate. Meeting of the Hungarian Society for Clinical Biostatistics, October 19, 2018.

2018.10.19. Presentation

The presentation gave an overview of methodological paradigm of “Big Data” and its relation to classic statistics. The debate concerned the problem’s relevance from a biostatistical point of view.

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