Peer-reviewed publications

  • Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Kleinen-von Königslöw, K. and 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. and 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. and 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. and 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. and Rieger, J. (2022). Finding scientific topics in continuously growing text corpora. Proceedings of the 3rd Workshop on Scholarly Document Processing. Link. GitHub. PsychTopics App.
  • Lange, K.-R., Rieger, J., Benner, N. and Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis. Proceedings. GitHub.
  • Rieger, J., Lange, K.-R., Flossdorf, J. and Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story’22 Workshop. CEUR-WS 3117, 5-13. Link. GitHub.
  • Rieger, J., Jentsch, C. and 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. and 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. and Rahnenführer, J. (2021). Die Entwicklung von Forschungssoftware als praktische Interdisziplinarität. M&K Medien & Kommunikationswissenschaft 69, 80-96. DOI.
  • Rieger, J., Jentsch, C. and 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. and 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. and Rieger, J. (2020). Distorted by Populism – A computational analysis of German parliamentarians’ linking practices on Twitter [Im Zerrspiegel des Populismus – Eine computergestützte Analyse der Verlinkungspraxis von Bundestagsabgeordneten auf Twitter]. Publizistik 65, 403-424. DOI. GitHub. EJO.

Recent submissions

  • Rieger, J., Jentsch, C. and Rahnenführer, J.: LDAPrototype: A Model Selection Algorithm to Improve Reliability of Latent Dirichlet Allocation. DOI.
  • Faymonville, M., Riffo, J., Rieger, J. and Jentsch, C.: spINAR: An R Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models. In review. arXiv.

Preprints

  • Lange, K.-R., Rieger, J. and Jentsch, C. (2022). Lex2Sent: A bagging approach to unsupervised sentiment analysis. arXiv. DOI.

Working paper

  • Schmidt, T., Müller, H., Rieger, J., Schmidt, T. and Jentsch, C. (2023). Inflation Perception and the Formation of Inflation Expectations. Ruhr Economic Papers #1025. DOI.
  • Shrub, Y., Rieger, J., Müller, H. and 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.

Non-peer-reviewed publications

  • Jentsch, C., Mammen, E., Müller, H., Rieger, J. and 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. and 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.

The inflation perception indicator

  • 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.
  • Müller, H., Rieger, J., Schmidt, T. and Hornig, N. (2022). An Increasing Sense of Urgency: The Inflation Perception Indicator (IPI) to 30 June 2022 - a Research Note. DoCMA Working Paper #12. DOI.
    • Galloping Inflation: Early Warning System Anticipates Significant Price Increase [Galoppierende Inflation: Frühwarnsystem rechnet mit deutlicher Preissteigerung]. Handelsblatt (07/25/2022). Link.
  • Müller, H., Rieger, J., Schmidt., T. and Hornig, N. (2022). Pressure is high - and rising. The Inflation Perception Indicator (IPI) to 30 April 2022 - a Research Note Analysis. DoCMA Working Paper #10. DOI.
    • Reminiscent of the 1970s - Early Warning System for Inflation at a Record High [Erinnert an 1970er Jahre - Frühwarnsystem für Inflation auf Rekordhoch]. Handelsblatt (05/24/2022). Link.
  • Müller, H., Schmidt., T., Rieger, J., Hufnagel, L.M. and Hornig, N. (2022). A German Inflation Narrative. How the Media frame Price Dynamics: Results from a RollingLDA Analysis. DoCMA Working Paper #9. DOI.
    • The I-Index: The New Measurement of Inflation [Der I-Index: Die neue Vermessung der Inflation]. Handelsblatt (03/10/2022). Link.

The uncertainty perception indicator

  • Müller, H., Rieger, J. and 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.
  • Müller, H., Rieger, J. and Hornig, N. (2021). Riders on the Storm. The Uncertainty Perception Indicator (UPI) in Q1 2021. DoCMA Working Paper #7. DOI.
  • Müller, H., Rieger, J. and Hornig, N. (2021). We’re rolling. Our Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a New Method for the Measurement of Evolving Economic NarrativesOur Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a New Method for the Measurement of Evolving Economic Narratives. DoCMA Working Paper #6. DOI.
  • Müller, H., Hornig, N. and Rieger, J. (2021). For the times they are a-changin’. Gauging Uncertainty Perception over Time. DoCMA Working Paper #3. DOI.

Other publications

  • Bittermann, A., Müller, S. M., and Rieger, J. (2022). PsychTopics: How to keep track of the psychology research landscape [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.