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Doctoral Student

M.Sc. Jonas Rieger


TU Dortmund University
Department of Statistics
Chair of Business and Social Statistics
CDI Building, Room 3
44221 Dortmund

E-Mail: riegerstatistik.tu-dortmundde
Phone: +49 231 755 3127

Portrait photo of Jonas Rieger © Felix Schmale​/​TU Dortmund
  • since December 2018: Doctoral Student
  • 2016-2018: Research Assistant in the field of Text Mining
  • 2016-2018: M.Sc. Statistics with minor in Computer Science, TU Dortmund University
    Master Thesis: Comparison of Latent Topics from LDA Topic Models [Vergleich latenter Themen aus LDA-Topicmodellen]
  • 2013-2016: B.Sc. Statistics with minor in Computer Science, TU Dortmund University

Member of the Dortmund Center for data-based Media Analysis (DoCMA).

  • Evaluation of topic models (quality and reliability)
  • Model selection and parameter tuning of topic models
  • Update algorithms for topic models
  • Detection of structural breaks, events and narratives in text corpora
  • Text corpus-based indicators
  • Content analysis of texts and tweets of political parties and parliamentarians

GitHub Profile

Please also have a look at the PsychTopics App, which is based on the RollingLDA method as well as The World of Topic Modeling in R (Wiedemann, 2022) for a short overview of several R packages dealing with topic modeling.


  • 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. pdf. 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.

Preprints and Working Papers

  • 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.
    Previous Editions"Riders on the Storm" (Q1 2021), "We’re rolling" (Q4 2020), "For the times they are a-changin'" (Q3 2020).
  • 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. GitHub. Handelsblatt.
    Previous Editions: A German Inflation Narrative (02/28/2022), Handelsblatt.
  • 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.

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.
  • 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).
  • Text as Data (WiSe 2022/23)
  • Einführung in LaTeX (SuSe 2022, 2021, 2020, 2019)
  • Data Mining Cup (SuSe 2022, 2021, 2020 [1st and 6th], 2019 [12th and 16th])
  • Fallstudien I (WiSe 2022)
  • Schätzen und Testen I (WiSe 2022)
  • Nichtparametrische Verfahren (WiSe 2021)
  • Seminar: Text Data meets Econometrics (WiSe 2021)
  • Entscheidungstheorie - Statistik VI (SuSe 2020)
  • Wahrscheinlichkeitstheorie - Statistik V (WiSe 2020)
  • Seminar: Textdatenanalyse (SuSe 2019)

Location & approach

The campus of TU Dort­mund Uni­ver­sity is located close to interstate junction Dort­mund West, where the Sauerlandlinie A45 (Frankfurt-Dort­mund) crosses the Ruhrschnellweg B1 / A40. The best interstate exit to take from A45 is “Dort­mund-Eichlinghofen” (closer to South Cam­pus), and from B1 / A40 “Dort­mund-Dorstfeld” (closer to North Cam­pus). Signs for the uni­ver­si­ty are located at both exits. Also, there is a new exit before you pass over the B1-bridge leading into Dort­mund.

For travelling to the Department of Statistics, convenient parking places can be found at Vogelpothsweg (Gates 21 / 24) or alternatively at the Otto-Hahn-Straße (Gates 28 / 30 / 35).

TU Dort­mund Uni­ver­sity has its own train station (“Dort­mund Uni­ver­si­tät”). From there, suburban trains (S-Bahn) leave for Dort­mund main station (“Dort­mund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The uni­ver­si­ty is easily reached from Bo­chum, Essen, Mülheim an der Ruhr and Duis­burg.

You can also take the bus or subway train from Dort­mund city to the uni­ver­si­ty: From Dort­mund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dort­mund Uni­ver­sity leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dort­mund main station to the stop “Dort­mund Kampstraße”. From there, take U43 or U44 to the stop “Dort­mund Wittener Straße”. Switch to bus line 447 and get off at “Dort­mund Uni­ver­si­tät S”.

The H-Bahn is one of the hallmarks of TU Dort­mund Uni­ver­sity. There are two stations on North Cam­pus. One (“Dort­mund Uni­ver­si­tät S”) is directly located at the suburban train stop, which connects the uni­ver­si­ty directly with the city of Dort­mund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Cam­pus) Eichlinghofen. The other station is located at the dining hall at North Cam­pus and offers a direct connection to South Cam­pus every five minutes.

The AirportExpress is a fast and convenient means of transport from Dort­mund Airport (DTM) to Dort­mund Central Station, taking you there in little more than 20 minutes. From Dort­mund Central Station, you can continue to the uni­ver­si­ty campus by interurban railway (S-Bahn). A larger range of in­ter­na­tio­nal flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the uni­ver­si­ty station.

Interactive map

The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".

Campus Lageplan Zum Lageplan