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      Ist die psychologische Forschung durchlässig für aktuelle gesellschaftliche Themen? : Eine szientometrische Analyse am Beispiel Flucht und Migration mithilfe von Topic Modeling

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          Abstract

          Zusammenfassung. Vor dem Hintergrund der Beziehung zwischen gesellschaftlichen Herausforderungen und entsprechenden Beiträgen der Wissenschaft wurde am Beispiel des Themenbereichs Flucht und Migration untersucht, inwiefern die psychologische Forschung jeweils aktuelle gesellschaftliche und politische Fragestellungen aufgreift, ob sie eine evidenzbasierte Grundlage für diese Fragestellungen schafft und welchen Teildisziplinen der Psychologie die Forschungsthemen zugeordnet werden können. Dazu wurden mit Structural Topic Modeling die Abstracts von 4.073 Publikationen aus den deutschsprachigen Ländern, dokumentiert in der psychologischen Referenzdatenbank PSYNDEX und veröffentlicht zwischen 1980 und 2017, analysiert. Es konnten 19 Themen identifiziert werden. Den stärksten zunehmenden Trend zeigten Traumatisierung von Flüchtlingen, transkulturelle Psychotherapie sowie die rehabilitative Behandlung von Patienten mit Migrationshintergrund. Im Bereich der Situation von Ausländern und Gastarbeitern, der Kriminalität von Jugendlichen sowie der sozialen Integration und Akkulturation mit Bezug zur ehemaligen DDR zeigte sich die deutlichste Abnahme der Wahrscheinlichkeit. Das Thema Sprachentwicklung von Migrantenkindern wies die höchste Wahrscheinlichkeit auf, von empirischen Studien behandelt zu werden, das Thema Identitätsentwicklung die höchste Wahrscheinlichkeit von nicht-empirischen Beiträgen. Zusammenfassend wird konstatiert, dass sich im Bereich von Flucht und Migration wesentliche gesellschaftliche und politische Entwicklungen in der psychologischen Fachliteratur widerspiegeln. Hinsichtlich empirischer Beiträge zu den Themen zeigt sich ein gemischtes Bild. Die meisten Themen haben einen klinisch-psychologischen Schwerpunkt, wobei jedoch auch andere Disziplinen vertreten sind. Methodisch kann festgehalten werden, dass der Topic-Model-Ansatz eine hilfreiche Methode für szientometrische Untersuchungen mit großen Textmengen darstellt.

          Does Psychological Research Address Current Social Issues? A Scientometric Analysis of the Example of Refugees and Migration Using Topic Modeling

          Abstract. In the context of the scientific investigation of social challenges, and drawing on the example of refugees and migration, we investigated to what extent psychological research takes up current social and political issues, whether it provides an evidence-based foundation for addressing social challenges, and to which subdisciplines of psychology the research topics can be assigned. Specifically, the abstracts of 4,073 publications from German-speaking countries, documented in the psychological reference database PSYNDEX and published between 1980 and 2017, were analyzed using structural topic modeling. A total of 19 topics were identified. The most strongly increasing trend was shown for topics concerning the traumatization of refugees, transcultural psychotherapy, and rehabilitative treatment. Topics concerning the situation of foreigners and guest workers, juvenile crime, as well as social integration and acculturation in relation to the former German Democratic Republic showed decreasing trends. The topic of language development of migrant children had the highest probability of being addressed by empirical studies; the topic of identity development was most probable for nonempirical contributions. In summary, important social and political developments in the field of refugees and migration are reflected in psychological literature. A mixed picture emerges with regard to empirical contributions on the topics. Most topics have a clinical-psychological focus, but other disciplines are also represented. From a methodological perspective, the topic modeling approach has proved to be a helpful method for scientometric investigations within large text corpora.

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          Most cited references14

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          Finding scientific topics.

          A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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            Latent Dirichlet allocation

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              Mining big data to extract patterns and predict real-life outcomes.

              This article aims to introduce the reader to essential tools that can be used to obtain insights and build predictive models using large data sets. Recent user proliferation in the digital environment has led to the emergence of large samples containing a wealth of traces of human behaviors, communication, and social interactions. Such samples offer the opportunity to greatly improve our understanding of individuals, groups, and societies, but their analysis presents unique methodological challenges. In this tutorial, we discuss potential sources of such data and explain how to efficiently store them. Then, we introduce two methods that are often employed to extract patterns and reduce the dimensionality of large data sets: singular value decomposition and latent Dirichlet allocation. Finally, we demonstrate how to use dimensions or clusters extracted from data to build predictive models in a cross-validated way. The text is accompanied by examples of R code and a sample data set, allowing the reader to practice the methods discussed here. A companion website (http://dataminingtutorial.com) provides additional learning resources. (PsycINFO Database Record
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                Author and article information

                Contributors
                Journal
                pru
                Psychologische Rundschau
                Hogrefe Verlag, Göttingen
                0033-3042
                2190-6238
                2019
                : 70
                : 4
                : 239-249
                Affiliations
                [ 1 ]Leibniz-Zentrum für Psychologische Information und Dokumentation (ZPID), Trier
                [ 2 ]EU-Forschungsberatungs- und Koordinierungsstelle, Hochschule Trier
                Author notes
                Dipl.-Psych. André Bittermann, Leibniz-Zentrum für Psychologische Information und Dokumentation (ZPID), Universitätsring 15, 54296 Trier, abi@ 123456leibniz-psychology.org
                Dr. Eva Maria Klos, Hochschule Trier, EU-Forschungsberatungs- und Koordinierungsstelle, Schneidershof, 54293 Trier
                Article
                pru_70_4_239
                10.1026/0033-3042/a000426
                08e7bb89-8ff4-415f-a3e2-6de9dd864043
                Veröffentlicht unter der Hogrefe OpenMind Lizenz (https://doi.org/10.1026/a000002)
                History
                Categories
                Originalarbeit

                Psychology
                refugees,Migration,Flucht,Szientometrie,Topic Modeling,Trends,migration,scientometrics,topic modeling,trends

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