New results of our project ‘NLP analysis of online depression forums’ was published in SSM Population Health (D1) written by Renáta Németh, Domonkos Sik and Eszter Katona. The asymmetries of the biopsychosocial model of depression in lay discourses – Topic modeling of online depression forums.
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.)
Gábor Csomor, Borbála Simonovits and Renáta Németh published their results in the journal Szociológiai Szemle with the following title: ” Discrimination at local governments? – Results of an online field experiment “.
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.
Further information of the lecture is available on the university’s website. The video recording of the presentation can be accessed on this link.
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.
On the workshop, Ildikó Barna and Árpád Knap presented the detection of various types of antisemitic narratives in articles and comments, some of which are not measurable by surveys.
The level of antisemitism in Hungary has always been among the highest in Europe. Representative surveys show that approximately 33 to 40 per cent of the Hungarian population is antisemitic. Although there has been some fluctuation, the level of antisemitism has remained quite stable. Moreover, we found, based on representative surveys among Hungarian Jews, that although the proportion of those having experienced or witnessed antisemitic acts one year prior to the survey decreased massively from 79 to 58 per cent between 1999 and 2017, the perception of antisemitism severely deteriorated. While in 1999, 37 per cent of Jews thought that antisemitism was strong or very strong in Hungary, in 2017 65 per cent said the same. This high discrepancy between experience and perception is due to several factors, being one of them the spread of online hatred. This fact makes the analysis of online sources necessary. Due to the vast amount of unstructured online textual data, their examination demands new tools, one of them being Natural Language Processing (NLP). NLP is an interdisciplinary field of research in the intersection of computer science, artificial intelligence, as well as linguistics. In our research, we apply NLP on a massive corpus of recent Hungarian news articles, social media content, and online forum comments. NLP makes possible not only the examination of the structure, the main topics, and actors of overt antisemitism but the identification of underlying subjects and specificities of latent antisemitism. In our paper, we present the first results of our research.
The post about the conference can be found here on our website.
In our research we aimed at gathering and automatically classifying online forum posts into the above three framing types by applying different supervised learning algorithms. As our dataset, we decided to use depression-related posts from the most popular English-speaking health forums within the time interval 2016-2018. We obtained only publicly available posts, which are shared willingly by their authors. We used Python to implement our analyses. After pre-processing and feature extraction, the scikit-learn library was used with different algorithms (SVM, Naive Bayes, Logistic Regression and Decision Trees). Our poster can be downloaded here.
Kostadinova, Tatiana; Kmetty, Zoltán: Corruption and Political Participation in Hungary: Testing Models of Civic Engagement. EAST EUROPEAN POLITICS AND SOCIETIES Online first p. 1 (2019)
Kmetty, Zoltán: Incumbent party support and perceptions of corruption – an experimental study. SZOCIOLÓGIAI SZEMLE 28 : 4 pp. 152-165., 14 p. (2018)
Kmetty, Zoltán: Korrupció percepciója, pártosság, választási részvétel: Hogyan változott a szavazók véleménye a hazai politikai korrupcióról a 2014-2018-as parlamenti ciklus alatt? In: Böcskei, Balázs; Szabó, Andrea (szerk.) Várakozások és valóságok. Parlamenti választás 2018. Budapest, Magyarország : Napvilág Kiadó, MTA TK PTI, (2018) pp. 292-316., 25 p.
Eszter Katona’s presentation
Eszter Katona held a presentation entitled ‘Natural Language Processing in Social Sciences’ on 10 May 2019 in Basel, at the Joint Annual Conference of the GPSA Methods of Political Science Section and the SPSA Empirical Methodology Working Group (https://www.methodology-dvpw-
Eszter, Rita Katona ; Renáta, Németh ; Zoltán, Kmetty (2019): Natural Language Processing in Social Sciences. Joint Annual Conference of the GPSA Methods of Political Science Section and the SPSA Empirical Methodology Working Group May 10/11, 2019.
Katona Eszter (2019): Szakdolgozat, 2018, ELTE, Survey Statisztika MSc
In this paper, we explore antisemitism in contemporary Hungary. After briefly introducing the different types of antisemitism, we show the results of a quantitative survey carried out in 2017 on a nationally representative sample. Next, we present the research we conducted on the articles related to Jews from the far-right site Kuruc.info. Our corpus contained 2,289 articles from the period between February 28, 2016, and March 20, 2019. To identify latent topics in the text, we employed one of the methods of Natural Language Processing (NLP), namely topic modeling using the LDA method. We extracted fifteen topics. We found that racial antisemitism, unmeasurable by survey research, is overtly present in the discourse of Kuruc.info. Moreover, we identified topics that were connected to other types of antisemitism.
Keywords: antisemitism, Hungary, Natural Language Processing, topic modeling, LDA
Ildiko Barna held a presentation entitled “Overt and Subtle Antisemitism in Hungary” at the “Antisemitism, Anti-Zionism, Israel, and the Holocaust” workshop in Salzburg on 23 February 2019, discussing the methods and challenges of measuring antisemitism. Besides presenting the results of surveys on antisemitism in Hungary, she talked about new research opportunities offered by Natural Language Processing (NLP) methods, and also about the work of the research group.
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.