Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Kleinen-von Königslöw, K., & Wiedemann, G. (2024, forthcoming). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Accepted for Studies in Communication and Media.
Banning, F. & Reale, J. & Roos, M. (2023). The Complexity of Corporate Culture as a Potential Source of Firm Profit Differentials. https://dx.doi.org/10.2139/ssrn.4508695
Boumans, D., Müller, H., & Sauer, S. (2023). How media content influences economic expectations: Evidence from a global expert survey. Journal of Forecasting. https://doi.org/10.1002/for.2961
Hibbeln, M., Kopp, R. M., Urban, N. (2023) Credit Risk Modeling in the Age of Machine Learning. http://dx.doi.org/10.2139/ssrn.3913710
Hibbeln M., Metzler R., Osterkamp, W. (2023) Not on the Same Page – (Text-)Complexity in European Securitizations. Working Paper. Presentation at CFC 2022, Banking Research Workshop 2022. https://dx.doi.org/10.2139/ssrn.4182623
Hibbeln M., Osterkamp, W. (2023) Simple is Simply not Enough – Features versus Labels of Complex Financial Securities. Working Paper. Presentation at Banking Research Workshop 2020, IDOC 2020, SWFA 2021, WEAI 2021, DGF 2021, FMA Europe 2021. https://dx.doi.org/10.2139/ssrn.3598123
Jentsch, C., & Lange, K.-R. (2023). SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments. Proceedings of the 3rd Workshop on Computational Linguistics for Political Text Analysis@KONVENS 2023.
Müller. H. (2023). Challenging Economic Journalism. Covering Business and Politics in an Age of Uncertainty. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-31030-0
Müller, H., Rieger. J., & Hornig, N. (2023). “We’re rolling”. Our Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a new Method for the Measurement of Evolving Economic Narratives. DoCMA Working Paper #6. http://dx.doi.org/10.17877/DE290R-21974
Müller, H., Schmidt, T., Rieger, J., & 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. http://dx.doi.org/10.17877/DE290R-23141
Rieger, J., Hornig, N., Schmidt, T., & 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. https://github.com/JonasRieger/mufin23/blob/master/paper.pdf
Roos, M. & Reccius, M. (2023). Narratives in economics. Journal of Economic Surveys, 0, 1–39. https://doi.org/10.1111/joes.12576
Schmidt, T., Müller, H., Rieger, J., Schmidt, T., & Jentsch, C. (2023). Inflation Perception and the Formation of Inflation Expectations. Ruhr Economic Papers #1025. http://dx.doi.org/10.4419/96973191
Lange, K.-R., Rieger, J., Benner, N., & Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis, 47-53. https://old.gscl.org/media/pages/arbeitskreise/cpss/cpss-2022/workshop-proceedings-2022/254133848-1662996909/cpss-2022-proceedings.pdf
Lange, K.-R., Reccius, M., Schmidt, T., Müller, H., Roos, M., & Jentsch, C. (2022). Towards extracting collective economic narratives from texts. Ruhr Economic Papers #963. https://doi.org/10.4419/96973127
Rieger, J., Lange, K.-R., Flossdorf, J., & Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story’22 Workshop. CEUR-WS 3117, 5-13. https://ceur-ws.org/Vol-3117/paper1.pdf
Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Königslöw, K. K. V., & Wiedemann, G. (2023). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. https://doi.org/10.48550/arXiv.2312.16975
Roos, M., Reale, J., Banning, F. (2022). A value-based model of job performance. PLOS ONE, 17. https://doi.org/10.1371/journal.pone.0262430
Benner, N., Lange, K.-R., & Jentsch, C. (2022). Named Entity Narratives. Ruhr Economic Papers #962. https://doi.org/10.4419/96973126
Müller, H., Schmidt, T., Rieger, J., Hufnagel, L. M., & Hornig, N. (2022). “A German Inflation Narrative – How the Media frame Price Dynamics: Results from a RollingLDA Analysis”. DoCMA Working Paper #9. https://doi.org/10.17877/de290r-22632
Blagov, B,, Müller, H., Jentsch, C., Schmidt, T. (2021). The investment narrative: Improving private investment forecasts with media data. Ruhr Economic Papers, No. 921. https://doi.org/10.4419/96973067
Jentsch, C., Lee, E. R., & Mammen, E. (2021). Poisson reduced rank models with an application to political text data. Biometrika, 108(2), 455-468. https://doi.org/10.1016/j.csda.2019.106813
Jentsch, C., Mammen, E., Müller, H., Rieger, J., & 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. https://doi.org/10.17877/de290r-22363
Müller, H. (2017). Populism, de-globalisation, and media competition: The spiral of noise. Central European Journal of Communication, 9, 1 (18), 64–78. https://doi.org/10.19195/1899-5101.10.1(18).5