Dr. Jonas Rieger

• seit Oktober 2024: Scientific Manager des TRR 391
• seit Oktober 2022: Postdoc
• 2022-2023: NLP Scientist am Leibniz-Institut für Medienforschung │Hans-Bredow-Institut (HBI)
• 2018-2022: Promotion in Statistik, TU Dortmund
• 2016-2018: Wissenschaftliche Hilfskraft im Bereich Text Mining
• 2016-2018: M.Sc. Statistik mit Schwerpunkt Informatik, TU Dortmund
• 2013-2016: B.Sc. Statistik mit Schwerpunkt Informatik, TU Dortmund

GitHub Profil

• topiclabels ([cran][github])
• rollinglda ([cran][github])
• ldaPrototype ([cran][github][joss])
• tosca ([cran][github])
• spINAR ([cran][github][joss])

Schauen Sie sich gerne auch die PsychTopics App an, die auf der Methode RollingLDA basiert, sowie The World of Topic Modeling in R (Wiedemann, 2022) für eine kurze Übersicht einiger R Pakete mit Bezug zu Topic Modeling.

ORCiD

• Rieger, J., Grönberg, L., Loschke, C. und Braungardt, S. (2025) Monitoring narratives about the energy transition in Germany: Social (in)justice in the energy transition - from the digital debate to the living world. Accepted for Proceedings of the Text2Story’25 Workshop.
• Müller, H., Blagov, B., Schmidt, T., Rieger, J. und Jentsch, C. (2025). The macroeconomic impact of asymmetric uncertainty shocks. The Journal of Economic Asymmetries 31(e00410). DOI.
• Loschke C., Braungardt, S. und Rieger, J. (2025). What motivates and demotivates energy savings in times of crisis? - An argument mining analysis using X/Twitter data. Energy Efficiency 18(4). DOI.
• Rieger, J., Jentsch, C. und Rahnenführer, J. (2024). LDAPrototype: A model selection algorithm to improve reliability of latent Dirichlet allocation. PeerJ Computer Science 10(2279). DOI.
• Lange, K.-R., Rieger, J. und Jentsch, C. (2024). Lex2Sent: A bagging approach to unsupervised sentiment analysis. Proceedings of the 20th KONVENS Conference, 281-291. Link.
• Faymonville, M., Riffo, J., Rieger, J. und Jentsch, C. (2024). spINAR: An R Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models. Journal of Open Source Software 9(97), 5386. DOI.
• Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Kleinen-von Königslöw, K. und Wiedemann, G. (2024). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Studies in Communication and Media 13, 72-100. DOI.
• Krause, C., Rieger, J., Flossdorf, J., Jentsch, C. und Beck, F. (2023). Visually Analyzing Topic Change Points in Temporal Text Collections. Vision, Modeling, and Visualization. DOI.
• Rieger, J., Hornig, N., Flossdorf, J., Müller, H., Mündges, S., Jentsch, C., Rahnenführer, J. und Elmer, C. (2023). Debunking Disinformation with GADMO: A Topic Modeling Analysis of a Comprehensive Corpus of German-language Fact-Checks. Proceedings of the 4th Conference on Language, Data and Knowledge. Link. GitHub.
• Rieger, J., Hornig, N., Schmidt, T. und Müller, H. (2023). Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling. Proceedings of the 3rd Workshop on Modelling Uncertainty in the Financial World. Link. GitHub.
• Bittermann, A. und Rieger, J. (2022). Finding scientific topics in continuously growing text corpora. Proceedings of the 3rd Workshop on Scholarly Document Processing. Link. GitHub. PsychTopics App. Poster.
• Lange, K.-R., Rieger, J., Benner, N. und Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis. pdf. GitHub.
• Rieger, J., Lange, K.-R., Flossdorf, J. und Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story'22 Workshop. CEUR-WS 3117, 5-13. pdf. GitHub.
• Rieger, J., Jentsch, C. und Rahnenführer, J. (2021). RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data. Findings of the Association for Computational Linguistics: EMNLP 2021, 2337-2347. DOI. GitHub.
• von Nordheim, G., Rieger, J. und Kleinen-von Königslöw, K. (2021). From the fringes to the core – An analysis of right-wing populists’ linking practices in seven EU parliaments and Switzerland. Digital Journalism. DOI. GitHub. EJO.
• von Nordheim, G., Koppers, L., Boczek, K., Rieger, J., Jentsch, C., Müller, H. und Rahnenführer, J. (2021). Die Entwicklung von Forschungssoftware als praktische Interdisziplinarität. M&K Medien & Kommunikationswissenschaft 69, 80-96. DOI.
• Rieger, J., Jentsch, C. und Rahnenführer, J. (2020). Assessing the Uncertainty of the Text Generating Process using Topic Models. ECML PKDD 2020 Workshops. CCIS 1323, 385-396. DOI. GitHub.
• Rieger, J. (2020). ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations. Journal of Open Source Software 5(51), 2181. DOI.
• Rieger, J., Rahnenführer, J. und Jentsch, C. (2020). Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype. Natural Language Processing and Information Systems, NLDB 2020. LNCS 12089, 118-125. DOI.
• von Nordheim, G. und Rieger, J. (2020). Im Zerrspiegel des Populismus – Eine computergestützte Analyse der Verlinkungspraxis von Bundestagsabgeordneten auf Twitter. Publizistik 65, 403-424. DOI. GitHub. EJO.

Aktuelle Einreichungen

• Rieger, J., Ruckdeschel, M. und Wiedemann, G.: PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning. OpenReview. arXiv.
• Schmidt, T., Rieger, J., Rahnenführer, J., Viciano, A. und Wormer, H.: Beyond fact-checking: Harnessing AI for news quality assessment.
• Schmidt, T., Müller, H., Rieger, J., Schmidt, T. und Jentsch, C.: Changing inflation perception and the propagation of news shocks.

Preprints und Working Paper

• Schmidt, T., Müller, H., Rieger, J., Schmidt, T. und Jentsch, C. (2023). Inflation Perception and the Formation of Inflation Expectations. Ruhr Economic Papers #1025. DOI.
• Shrub, Y., Rieger, J., Müller, H. und Jentsch, C. (2022). Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators. Ruhr Economic Papers #964. DOI.

Nicht peer-reviewed Publikationen

• Müller, H., Schmidt, T., Rieger, J., Hornig, N. and Hufnagel, L.M. (2023). The Inflation Attention Cycle: Updating the Inflation Perception Indicator (IPI) up to February 2023 - a Research Note. DoCMA Working Paper #13. DOI. GitHub.
  Vorherige Ausgaben und zusätzliche Links: Handelsblatt (07/25/2022), An Increasing Sense of Urgency (06/30/2022), Handelsblatt (05/24/2022), Pressure is high (04/30/2022), Handelsblatt (03/10/2022), A German Inflation Narrative (02/28/2022).
• Müller, H., Rieger, J. und Hornig, N. (2022). Vladimir vs. the Virus - a Tale of two Shocks. An Update on our Uncertainty Perception Indicator (UPI) to April 2022 - a Research Note. DoCMA Working Paper #11. DOI. GitHub.
  Vorherige Ausgaben: "Riders on the Storm" (Q1 2021), "We’re rolling" (Q4 2020), "For the times they are a-changin'" (Q3 2020).
• Jentsch, C., Mammen, E., Müller, H., Rieger, J. und Schötz, C. (2021). Text mining methods for measuring the coherence of party manifestos for the German federal elections from 1990 to 2021. DoCMA Working Paper #8. DOI. Spiegel Online.
• Rieger, J. und von Nordheim, G. (2021). corona100d – German-language Twitter dataset of the first 100 days after Chancellor Merkel addressed the coronavirus outbreak on TV. DoCMA Working Paper #4. DOI. GitHub.

Weitere Publikationen

• Bittermann, A., Müller, S. M., und Rieger, J. (2022). PsychTopics: Wie man den Überblick über die Forschungslandschaft der Psychologie behält. Open Password. Link.
• Rieger, J. (2019). Mónica Bécue-Bertaut (2019): Textual Data Science with R. Statistical Papers 60, 1797-1798. DOI.

Dissertation

• Rieger, J. (2022). Reliability evaluation and an update algorithm for the latent Dirichlet allocation. Dissertation. TU Dortmund University. DOI.

Eingeladene Vorträge

• Detecting narration changes in economics utilizing continuous topic modeling and (large) language models. Research Colloquium of the Faculty of Business Studies and Economics, JLU. Gießen, Germany (01/2025).
• Monitoring (social) media narratives combining retrospective few-shot classification with continuous topic modeling. CMStatistics 2024. London, England (12/2024).
• PETapter: A masked-language-modeling classification head for modular fine-tuning of (large) language models. CompStat 2024. Gießen, Germany (08/2024).
• Bekämpfung von Desinformation und Fake-News durch GADMO. BDI/BDA-Arbeitskreis Statistik. Düsseldorf, Germany (05/2024).
• Keep rollin’! The abilities for monitoring growing corpora using RollingLDA. ZPID Lecture Series. Trier, Germany (12/2022). Recording.

Beiträge zu Konferenzen und Workshops

• Exploring the potential of large language models (such as GPT-4) for (semi-)automatic content analysis of stances and frames in media texts. DGPuK 2024. Erfurt, Germany (03/2024). Abstract.
• Few-shot learning for automated content analysis (FLACA) in the German media debate on arms deliveries to Ukraine. Digital Total. Hamburg, Germany (10/2023). Poster.
• Debunking Disinformation with GADMO: A Topic Modeling Analysis of a Comprehensive Corpus of German-language Fact-Checks. DiTox'23 Workshop @LDK 2023. Vienna, Austria (09/2023).
• Bekämpfung von Desinformation durch GADMO: Analyse eines umfassenden deutschsprachigen Faktencheck-Korpus mithilfe von Topic Modellen. Statistische Woche 2023. Dortmund, Germany (09/2023).
• Scrutinizing ChatGPT against Few-Shot Learning with Adapter Extensions and XLM-RoBERTa: A Case Study on Identifying Claims, Arguments and their Stance in the German News Media Debate on Arms Deliveries to Ukraine. Statistische Woche 2023. Dortmund, Germany (09/2023).
• Beyond "Master Frames": A Semi-automated Approach to Studying Viewpoint Diversity of the Media Discourse. ECREA PolComm 2023. Berlin, Germany (08/2023).
• Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. DGPuK 2023. Bremen, Germany (05/2023).
• Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling. MUFin'23 Workshop @AAAI 2023. Washington, DC, USA (02/2023). Pre-recording.
• Finding scientific topics in contionuously growing text corpora. SDP'22 Workshop @COLING 2022. Gyeongju, Republic of Korea (10/2022). Pre-recording.
• Monitoring consistent topics in continuously growing scientific text corpora. Statistische Woche 2022. Münster, Germany (09/2022).
• Dynamic change detection in topics based on rolling LDAs. Text2Story'22 Workshop @ECIR 2022. Stavanger, Norway (04/2022). Pre-recording.
• Improving the reliability of LDA results using LDAPrototype as selection criterion. DAGStat 2022. Hamburg, Germany (03/2022).
• RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data. EMNLP 2021. Punta Cana, Dominican Republic (11/2021). Pre-recording.
• Assessing the Uncertainty of the Text Generating Process using Topic Models. EDML'20 Workshop @ECML PKDD 2020. Online (09/2020).
• Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype. NLDB 2020. Online (06/2020).
• Quantifizierung der Stabilität der Latent Dirichlet Allocation mithilfe von Clustering auf wiederholten Durchläufen. Statistische Woche 2019. Trier, Germany (09/2019).
• Softwaretools für die Kommunikationsforschung. DGPuK 2019. Münster, Germany (05/2019).
• Measuring Stability of Replicated LDA Runs. DAGStat 2019. Munich, Germany (03/2019).

• Natural Language Processing (WiSe 2024/25, 2023/24)
• Data Mining Cup (SoSe 2024, 2023, 2022, 2021, 2020, 2019)
• Text as Data (WiSe 2022/23)
• Einführung in LaTeX (SoSe 2022, 2021, 2020, 2019)
• Fallstudien I (WiSe 2021/22)
• Schätzen und Testen (WiSe 2021/22)
• Nichtparametrische Verfahren (WiSe 2020/21)
• Seminar: Text Data meets Econometrics (WiSe 2020/21)
• Entscheidungstheorie - Statistik VI (SoSe 2020)
• Wahrscheinlichkeitstheorie - Statistik V (WiSe 2019/20)
• Seminar: Textdatenanalyse (SoSe 2019)