Following a previous presentation, Eszter Katona, a member of our research group, was invited to the ICSA (International Conference on Sustainability Analysis) conference on 15.07.2022. The panel is entitled “Challenges and recent advancements in corruption risk assessment”. Eszter presented her research with her co-supervisor, Mihály Fazekas.
Zsófia Rakovics and Márton Rakovics will display the results of their research entitled “Semantic evolution of words in Hungarian PM Viktor Orbán’s speeches using a temporal word embedding model focusing on the issue of migration” at the poster section of the 8th International Conference on Computational Social Science IC2S2 (The University of Chicago Booth School of Business, Chicago, IL, USA), July 19-22, 2022.
Domonkos Sik gave a presentation entitled “Populist Juggling with Fear – the Case of Hungary” at The End of “Freedom” in Central and Eastern Europe? Addressing the Challenges of an Illiberal Turn (9-11. June 2022) conference organized by Andrássy University Budapest.
Eszter Katona gave a presentation at the annual conference of the Centre for Social Sciences, entitled “Corruption risk in public tenders: using text mining to detect restrictions on competition ” on 3 June, 2022.
Zsófia Rakovics gave a presentation at the annual conference of the Centre for Social Sciences, entitled “Analyzing the semantic evolution of words related to migration in prime ministerial speeches using a temporal word embedding model” on 3 June, 2022.
Tímea Emese Tóth gave a presentation entitled “Analysis of the Twitter discourse on sustainability using the method of natural language processing” at the Centre for Social Sciences’ annual conference on June 3, 2022. The performance was a great success, with Emese receiving the Special Award for the Best Doctoral Presentation in the non-doctoral section.
A visualisation of Fanni, Eszter and Árpád’s previous research was also exhibited at this year’s Data Stories event. https://datastoriesceu.org/gallery/happy-birthday-information-society
The primary aim of the research is to use NLP tools to review the themes that the journal has introduced into the domestic discourse of “information society studies” over the past 15 years, and to explore the thematic structure of the journal. In addition to content analysis, the research provides insights into the co-authorship network of the journal’s contributors and the relationship between authors and specific topics.
The latest publication by Ildikó Barna and Árpád Knap was published in the journal Nationalities Papers (D1). In their paper, they examined the thematic structure and discursive framing in newspaper articles related to the Trianon Peace Treaty and the Holocaust using LDA topic models and qualitative analysis. The article is open access.
Researchers of our project Digital Lens Alexandra and Eszter are co-presenting at Workshop on NS Camps and Killing Sites in Zagreb on the ongoing topographical research of exclusion and inclusion among Holocaust survivors as understood from the DEGOB database of survivor testimonies.
Eszter Katona, a member of our research group, gave a presentation at the ‘There’s something new under the sun’ conference on innovative methods. Eszter’s presentation was titled ‘Using algorithms to track corruption. Applications of natural language processing in corruption research’, and was related to her doctoral research.
Zsófia Rakovics gave a presentation entitled Demonstrating the potential of Temporal Positive Pointwise Mutual Information (TPPMI) temporal word embedding model – Semantic evolution of words in prime ministerial speeches at the ’Van új a nap alatt’ conference of the ELTE Angelusz Róbert College for Advanced Studies in Social Sciences, 2022.04.22. The work will also be published in the accompanying conference proceedings (in print).
Eszter Katona, a member of our research team, also presented a paper co-authored by her co-chair at the ECPR workshop on measuring corruption. Their presentation, ‘Hidden barriers to open competition: using text mining to uncover corrupt restrictions to competition in Public Procurement’, is related to Eszter’s PhD research.
A new publication by members of our research team (Árpád Knap, Diána Bartha, Ildikó Barna) on the memory politics of Trianon and the Holocaust using NLP. Within the “European Memory Politics – Populism, Nationalism and the Challenges to a European Memory Culture (EuMePo)” international research network supported by Jean Monnet Network Grant. English abstract is also available on the link, and an English-language paper on this research is also in press.
The Journal Egészségfejlesztés requested a short report for Hungarian professionals about the results of our research series in the topic of online depression forums.
Domonkos Sik, our team member, has just published a new book by Routledge summarizing ten years of research on social suffering: https://www.routledge.com/Empty-Suffering-A-Social-Phenomenology-of-Depression-Anxiety-and-Addiction/Sik/p/book/9781032045573
Domonkos leads our research team’s analysis of online depression forums: https://rc2s2.elte.hu/en/project/discursive-framing-of-depression-in-online-health-communities/
The poster „Using text mining for analysis of public procurement contracts” by Eszter Katona and Mihály Fazekas was displayed at the conference „11th Annual Conference on New Directions in Analyzing Text as Data”.
A publication of our research group has been published by Journal of Mental Health (IF 4.3).
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This paper presents a thematic analysis of the representation of corruption in the Hungarian online media, using a text mining tool called dynamic topic modeling. The text corpus was provided by K-Monitor and includes online articles on corruption and issues related to the misuse of public funds. Our study is exploratory in nature: it is aimed at identifying the main topics of the articles and the dynamics of thematic changes in the period 2007–2018, including the meaning, the background and the changes of each corruption topic. The causal links revealed by this research lie in whether the medium is of an oppositional or of a pro-government position, and how election campaign periods affect the thematic structure of the representation of corruption. Owing to the fact that the ownership of the news portal Origó changed during the analysed period, a natural experiment has also been possible in an attempt to reveal the impact of this change on the thematic structure of the corruption discourse on the portal in question.
Three members of our research group: Eszter Katona, Árpád Knap and Fanni Máté, with the contribution of Mihály Csótó, wrote an article for the special anniversary issue of the Information Society journal. The primary aim of the study is to review the topics that the journal has included in the Hungarian discourse of “information society studies” over the past 15 years and to explore the thematic structure of the journal with NLP methods. In addition to the content analysis, the article also provides an insight into the co-author network of the journal, as well as the relationship between the authors and each topic.
Nowadays, online communities are typical sources of social support, which is a considerable help especially for those suffering from depression or anxiety. The aim of my research is to investigate the patterns of social support on an online depression and anxiety forum and to serve as an exploratory research of Natural Language Processing usage to classify comments into the categories of social support. The uniqueness of my research is the quantitative text analysis based on a complete qualitative analysis of the whole dataset. The conclusions of the qualitative analysis provide profound information for model definition, and for their evaluation. This knowledge is important for the investigation the potential of automatic text analysis in sociology. On average, four out of five comments are related to social support on the examined forum. Informational support appears in 59.9 percent of the supportive comments, while emotional support appears in 44.7 percent. The applied models’ accuracies are nearly 80 percent, which means that they classified the vast majority of comments into the right category. The results show that there is a potential in building reliable models in order to classify the comments into the previously defined categories of social support.
Sik Domonkos: From mental disorders to social suffering: Making sense of depression for critical theories. EUROPEAN JOURNAL OF SOCIAL THEORY (2018)
Sik, Domonkos: Válaszok a szenvedésre: A hálózati szolidaritás elmélete. Budapest, Magyarország : ELTE Eötvös Kiadó (2018) , 228 p.
Sik, Domonkos: A szenvedés határállapotai: Egy kritikai hálózatelmélet vázlata. Budapest, Magyarország : ELTE Eötvös Kiadó (2018) , 246 p.
Deckovic-Dukres, V., Hrkal, J., Németh, R., Vitrai, J., Zach, H.: Inequalities in health system responsiveness. Joint World Health Survey Report Based on Data from Selected Central European Countries, 2007. Jelentés a WHO megbízásából.
Remák, E., Gál, R.I., Németh, R.: Health and morbidity in the accession countries. Country report – Hungary. ENEPRI Research Reports 28, Brussels: ENEPRI, 2006.
Albert, F., Dávid, B., Németh, R.: Social support, social cohesion. In.: National Health Interview Survey 2003, Research Report, 2005. (Hung.)
(magyarul: Albert Fruzsina, Dávid Beáta, Németh Renáta: Társas támogatottság, társadalmi kohézió. In.: Országos Lakossági Egészségfelmérés OLEF2003, Kutatási Jelentés, 2005.)
The article aims at analysing online depression forums enabling lay reinterpretation and criticism of expert biomedical discourses. Firstly, two contrasting interpretations of depression are reconstructed: expert psy-discourses are confronted with the phenomenological descriptions of lay experiences, with a special emphasis on online forums as empirical platforms hosting such debates. After clarifying the general theoretical stakes concerning contested ‘depression narratives’, the results of an online ethnography are introduced: the main topics appearing in online discussions are summarised (analysing how the abstract tensions between lay and expert discourses appear in the actual discussions), along with the idealtypical discursive logics (analysing pragmatic advises, attempts of reframing self-narratives and expressions of unconditional recognition). Finally, based on these analyses an attempt is made to explore the latent functionality of online depression forums by referring to a secular ‘ritual healing’ existing as an unreflected, contingent potential.
Social scientists of mixed-methods research have traditionally used human annotators to classify texts according to some predefined knowledge. The ‘big data’ revolution, the fast growth of digitized texts in recent years brings new opportunities but also new challenges. In our research project, we aim to examine the potential for natural language processing (NLP) techniques to understand the individual framing of depression in online forums. In this paper, we introduce a part of this project experimenting with NLP classification (supervised machine learning) method, which is capable of classifying large digital corpora according to various discourses on depression. Our question was whether an automated method can be applied to sociological problems outside the scope of hermeneutically more trivial business applications.
The present article introduces our learning path from the difficulties of human annotation to the hermeneutic limitations of algorithmic NLP methods. We faced our first failure when we experienced significant inter-annotator disagreement. In response to the failure, we moved to the strategy of intersubjective hermeneutics (interpretation through consensus). The second failure arose because we expected the machine to effectively learn from the human-annotated sample despite its hermeneutic limitations. The machine learning seemed to work appropriately in predicting bio-medical and psychological framing, but it failed in case of sociological framing. These results show that the sociological discourse about depression is not as well founded as the bio medical and the psychological discourses – a conclusion which requires further empirical study in the future. An increasing part of machine learning solution is based on human annotation of semantic interpretation tasks, and such human-machine interactions will probably define many more applications in the future. Our paper shows the hermeneutic limitations of ‘big data’ text analytics in the social sciences, and highlights the need for a better understanding of the use of annotated textual data and the annotation process itself.
The supplementary material of this article can be found here.
In our paper, we present an overview of Natural Language Processing (NLP) methods, which developed parallel with the spread of ‘Big Data’ paradigm. We present the most promising methods for social sciences, the specific research questions they can answer and the methodological features that distinguish them from classic quantitative methods. These methods go far beyond classic quantitative text analysis based on simple word frequencies. Their modelling logic arises from machine learning methods; hence, it is substantially differing from the classic social science logic that seeks for explanation and casual effects. Our goal is to inspire Hungarian social scientists by providing an insight into a less-institutionalized area, since we believe that at an international level, text mining will be a standard method for empirical social science research within a few years.
Ildikó Barna, co-leader of our research group, gave a presentation on contemporary Hungarian antisemitism at the Formal and Applied Linguistics Institute of Charles University Prague. The presentation was based on the Online Antisemitism project conducted with Árpád Knap. In addition to presenting the results of the research so far, in her lecture she also discussed why sociological and domain knowledge is indispensable for interpreting the output of natural language processing.
The post about the presentation can be found here on our website.
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 computing capacity. These advances provide opportunities. Social behavior can be directly observed, not only on self-reported basis. The observation and analysis could happen in real-time, and – because of the development of NLP methods – the understanding of the content is getting deeper.
As our paper shows, there are new possibilities for sociological research which are in some sense just byproduct 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, addressing also the question of new skills acquired from traditionally trained sociologists.