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Der Suchbegriff 'bachl' ergab 4 Treffer.

  • 28649Colloquium
    BA/MA-Colloquium Digitale Forschungsmethoden (Marko Bachl)
    Zeit: Mi 12:00-14:00 (Erster Termin: 15.10.2025)
    Ort: Ihnestr.22/UG 3 Seminarraum (Ihnestr. 22)

    Kommentar

    Bitte beachten Sie die Vorgaben des IfPuK zu Anmeldung und Leistungen im Abschluss-Colloquium: deutsch, englisch.

    Das Colloquium begleitet Studierende auf dem Weg zu ihrer Bachelor- und Masterarbeit. Studierende aus den Studiengängen Publizistik- und Kommunikationswissenschaft (Bachelor & Master), Medien und Politische Kommunikation und Medieninformatik (Bachelor & Master) können teilnehmen, wenn sie ihre Abschlussarbeit an der Arbeitsstelle Digitale Forschungsmethoden schreiben möchten. Wir interessieren uns für zwei Arten von Abschlussarbeiten:


    1. Empirische Arbeiten, in denen Methoden eingesetzt werden, um inhaltliche Forschungsfragen zu beantworten


    2. Methodisch orientierte Arbeiten, in denen Forschungsmethoden untersucht werden

    Studierende können sich gerne mit ersten Themenideen oder konkreteren Projektvorschlägen bei uns melden. Das ist aber keine Voraussetzung. Wir unterstützen ebenso gerne bei der Themenfindung und -konkretisierung. Eine Liste mit möglichen Themen wird im Colloquium geteilt.

    Zu Beginn des Semesters werden formale Anforderungen und Bewertungskriterien vorgestellt. Im weiteren Verlauf des Semesters haben die Studierenden haben die Gelegenheit, ihr Projekt vorzustellen, über ihre Fortschritte zu berichten und dabei Rückmeldungen aus dem Kurs und von den Dozierenden zu erhalten. Außerdem werden nach Bedarf Techniken des wissenschaftlichen Arbeitens und methodische Fragen besprochen.

    Wenn Sie vorhaben, Ihre Abschlussarbeit an der Arbeitsstelle Digitale Forschungsmethoden zu schreiben, melden Sie sich bitte für das Colloquium an und schreiben Sie eine kurze E-Mail an marko.bachl@fu-berlin.de. Bei einer Betreuungszusage vor dem 30.9.2025 ist eine bevorzugte Aufnahme ins Colloquium möglich.

    Course language: Candidates can write their Bachelor’s or Master’s thesis in German or English. Office hours are in German or English. The main language of the colloquium is German. However, candidates can present their thesis in English if they prefer.

  • 28830Vorlesung
    Methoden I: Methoden der empirischen Kommunikations- und Medienforschung (Marko Bachl)
    Zeit: Mo 10:00-12:00 (Erster Termin: 13.10.2025)
    Ort: HFB/B Hörsaal (Garystr. 35-37)

    Kommentar

    Die beiden Vorlesungen des Moduls vermitteln fortgeschrittene Kenntnisse zu Methoden der empirischen Kommunikations- und Medienforschung. Nach einer Wiederholung zentraler Grundbegriffe der quantitativen Datenanalyse und der uni- und bivariaten Statistik besprechen wir moderne Verfahren der Datenerhebung, innovative Forschungsdesigns, Techniken der Computational Communication Science und ausgewählte multivariate Analyseverfahren. Die Inhalte vertiefen wir mit Beispielstudien aus der Kommunikations- und Medienforschung. Neben Konzepten und Interpretation behandeln wir auch die praktische Durchführung einiger Techniken und Analysen mit der Software R und RStudio. Die Vorlesung wird im Sommersemester 2026 fortgesetzt.

    Das Modul wird mit einer Klausur über beide Teile der Vorlesung am Ende des Sommersemesters abgeschlossen.

  • 28831Methodenübung
    AI-powered content analysis: Using generative AI to measure media and communication content (Marko Bachl)
    Zeit: Mo 14:00-16:00 (Erster Termin: 13.10.2025)
    Ort: Garystr.55/302a Seminarraum (Garystr. 55)

    Zusätzl. Angaben / Voraussetzungen

    • Prior knowledge in R, applied data analysis, and interacting with application programming interfaces (API) will be helpful but are not required. However, a willingness to learn the necessary skills and an openness to explore the possibilities of code-based computational social science research during the seminar are mandatory.

    • Some prior exposure to (standardized, quantitative) content analysis will be helpful. However, qualitative methods also have their place in evaluating content analysis methods. If you have little experience with the former but can contribute with the latter, make sure to team up with a student whose skill set complements yours.

    • Students will use their own computer and the software R and RStudio to follow along the practical part of the seminar. A browser-based solution will be provided for students who cannot install the software on their own devices.

    Kommentar

    Large language models (LLM; starting with Google’s BERT) and particularly their implementations as generative or conversational AI tools (e.g., OpenAI’s ChatGPT) are increasingly used to measure or classify media and communication content. The idea is simple yet intriguing: Instead of training and employing humans for annotation tasks, researchers describe the concept of interest to a model such as ChatGPT, present the coding unit, and ask for a classification. The first tests of the utility of ChatGPT and similar tools for content analysis were positive to enthusiastic [1–3]. User-friendly tutorials have proliferated the method to the average social scientist [4, 5]. Yet (closed-source, commercial) large language models are not entirely understood even by their developers, and their uncritical use has been criticized on ethical grounds [7–9].

    In this seminar, we will engage practically with this cutting-edge research method. We start with a quick refresher on the basics of quantitative content analysis (both human and computational) and an overview of the rapidly developing literature on LLMs’ utility in this field. The main part of the seminar will be dedicated to learning step-by-step how to use and evaluate a generative AI model for applied content analytical research. In the end, students should be able to use the method in their own research.

    Literaturhinweise

    1] Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30), e2305016120. https://doi.org/10/gsqx5m

    [2] Heseltine, M., & Clemm von Hohenberg, B. (2024). Large language models as a substitute for human experts in annotating political text. Research & Politics, 11(1). https://doi.org/10/gtkhqr

    [3] Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024). GPT is an effective tool for multilingual psychological text analysis. Proceedings of the National Academy of Sciences, 121(34), e2308950121. https://doi.org/10/gt7hrw

    [4] Törnberg, P. (2024). Best practices for text annotation with large language models. Sociologica, 18(2), Article 2. https://doi.org/10/g9vgm7

    [5] Stuhler, O., Ton, C. D., & Ollion, E. (2025). From codebooks to promptbooks: Extracting information from text with generative large language models. Sociological Methods & Research. https://doi.org/10/g9vgnq

    [7] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10/gh677h

    [8] Spirling, A. (2023). Why open-source generative AI models are an ethical way forward for science. Nature, 616(7957), 413–413. https://doi.org/10/gsqx6v

    [9] Widder, D. G., Whittaker, M., & West, S. M. (2024). Why ‘open’ AI systems are actually closed, and why this matters. Nature, 635(8040), 827–833. https://doi.org/10/g8xdb3

  • 28831aMethodenübung
    AI-powered content analysis: Using generative AI to measure media and communication content (Marko Bachl)
    Zeit: Mo 14:00-16:00 (Erster Termin: 13.10.2025)
    Ort: Garystr.55/302a Seminarraum (Garystr. 55)

    Kommentar

    Large language models (LLM; starting with Google’s BERT) and particularly their implementations as generative or conversational AI tools (e.g., OpenAI’s ChatGPT) are increasingly used to measure or classify media and communication content. The idea is simple yet intriguing: Instead of training and employing humans for annotation tasks, researchers describe the concept of interest to a model such as ChatGPT, present the coding unit, and ask for a classification. The first tests of the utility of ChatGPT and similar tools for content analysis were positive to enthusiastic [1–3]. User-friendly tutorials have proliferated the method to the average social scientist [4, 5]. Yet (closed-source, commercial) large language models are not entirely understood even by their developers, and their uncritical use has been criticized on ethical grounds [7–9]. In this seminar, we will engage practically with this cutting-edge research method. We start with a quick refresher on the basics of quantitative content analysis (both human and computational) and an overview of the rapidly developing literature on LLMs’ utility in this field. The main part of the seminar will be dedicated to learning step-by-step how to use and evaluate a generative AI model for applied content analytical research. In the end, students should be able to use the method in their own research. Requirements: Prior knowledge in R, applied data analysis, and interacting with application programming interfaces (API) will be helpful but are not required. However, a willingness to learn the necessary skills and an openness to explore the possibilities of code-based computational social science research during the seminar are mandatory. Some prior exposure to (standardized, quantitative) content analysis will be helpful. However, qualitative methods also have their place in evaluating content analysis methods. If you have little experience with the former but can contribute with the latter, make sure to team up with a student whose skill set complements yours. Students will use their own computer and the software R and RStudio to follow along the practical part of the seminar. A browser-based solution will be provided for students who cannot install the software on their own devices. References: [1] Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30), e2305016120. https://doi.org/10/gsqx5m [2] Heseltine, M., & Clemm von Hohenberg, B. (2024). Large language models as a substitute for human experts in annotating political text. Research & Politics, 11(1). https://doi.org/10/gtkhqr [3] Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024). GPT is an effective tool for multilingual psychological text analysis. Proceedings of the National Academy of Sciences, 121(34), e2308950121. https://doi.org/10/gt7hrw [4] Törnberg, P. (2024). Best practices for text annotation with large language models. Sociologica, 18(2), Article 2. https://doi.org/10/g9vgm7 [5] Stuhler, O., Ton, C. D., & Ollion, E. (2025). From codebooks to promptbooks: Extracting information from text with generative large language models. Sociological Methods & Research. https://doi.org/10/g9vgnq [7] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? ??. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10/gh677h [8] Spirling, A. (2023). Why open-source generative AI models are an ethical way forward for science. Nature, 616(7957), 413–413. https://doi.org/10/gsqx6v [9] Widder, D. G., Whittaker, M., & West, S. M. (2024). Why ‘open’ AI systems are actually closed, and why this matters. Nature, 635(8040), 827–833. https://doi.org/10/g8xdb3