Curriculum Vitae of Daniel Röchert, M.Sc.

Daniel Röchert, M.Sc.

Researcher

Address: Forsthausweg 2, 47057 Duisburg
Room: LF 218
Office hours: by arrangement
Phone: +49 203 379 - 2630
E-Mail: daniel.roechert@uni-due.de

Areas of Interest:

  • Social Media Analytics
  • Machine Learning & Deep Learning
  • Predictive Analytics
  • Information Systems

Academic Qualifications

2016-2017

M. Sc., Information Engineering and Computer Science, Rhine-Waal University of Applied Science, Germany

2012-2016

B. Sc., Media Communication and Computer Science, Rhine-Waal University of Applied Science, German

Professional Background

Since 2018

Researcher in the Junior Research Group "Digital Citizenship in Network Technologies" (DICINT), University of Duisburg-Essen

2017

Student research assistant - Cognitive Computing in z/OS mainframe system – Predictive Analytics: IBM Deutschland Research and Development GmbH, Böblingen

2017

Student research assistant - Cognitive Computing in z/OS mainframe system – Predictive Analytics: IBM Deutschland Research and Development GmbH, Böblingen

2016

Student research assistant - Database Management Systems: Fraunhofer Institute for Microelectronic Circuits and Systems (IMS), Duisburg

2015-2016

Student research assistant - Web Application Engineering: Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart

 

2015

Student trainee - Web Application Engineering: Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart

2014 Student research assistant - IT-Support: Rhine-Waal University of Applied Science, Kamp-Lintfort

Publications

Journal Articles and Conference Proceedings (with peer review)

Röchert, D., Neubaum, G., Ross, B.,  Brachten, F., & Stieglitz, S (2020). Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network AnalysisComputational Communication Research (CCR) 2(1), pp. 81–108, DOI: https://doi.org/10.5117/CCR2020.1.004.ROCH.

Röchert, D., Weitzel, M., Ross, B. (2020). The homogeneity of right-wing populist and radical content in YouTube recommendations. In International Conference on Social Media and Society (SMSociety ’20), Toronto, July 2020, 245-254. DOI: https://doi.org/10.1145/3400806.3400835. 

Röchert, D., Neubaum, G.Stieglitz, S (2020)Identifying Political Sentiments on YouTube: A Systematic Comparison regarding the Accuracy of Recurrent Neural Network and Machine Learning Models. In 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM2020), Leiden, 2020. DOI: https://doi.org/10.1007/978-3-030-61841-4_8.

Röchert, D., Shahi, GK., Neubaum, G., Ross, B., & Stieglitz, S (2021). The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic. Online Social Networks and Media (OSNEM), DOI: https://doi.org/10.1016/j.osnem.2021.100164.

Cabrera, B., Ross, B., Röchert, D., Brünker, F. & Stieglitz, S (2021). The influence of community structure on opinion expression: an agent-based model. J Bus Econ. https://doi.org/10.1007/s11573-021-01064-7​

Röchert, D., Neubaum, G., Ross, B. & Stieglitz, S (2022). Caught in a networked collusion? Homogeneity in conspiracy-related discussion networks on YouTubeInformation Systems, DOI: https://doi.org/10.1016/j.is.2021.101866.

Röchert, Cargnino, & Neubaum (2022). Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks. Journal of Computational Social Science. https://doi.org/10.1007/s42001-022-00161-z

 

Conference Presentations

Röchert, D., Shahi, GK., Neubaum, G. & Stieglitz, S. (2022). Political Polarization in Times of Crisis: Ideological Bias of News Coverage of the COVID-19 Pandemic on YouTube. International Communication Association (ICA), Paris, May 2022

Röchert, D., Cargnino, M., & Neubaum, G. (2021). When the Leader takes it all: An Agent-Based Model on the Effects of Ambivalent Opinion Leaders. 71st Conference of the International Communication Association (ICA), Virtual Meeting, 27th – 31st May 2021.

Röchert, D., Neubaum, G., Ross, B., & Stieglitz, S (2020, May). Caught in a Networked Complot? Analyzing Homogeneity in Conspiracy-Related Discussion Networks on YouTube. Paper presented at the Annual Meeting of the International Communicaton Association (ICA), Converted from Australia to virtual due to COVID-19, May 2020.

Röchert, D., Neubaum, G.Stieglitz, S (2020, May). Identifying Political Sentiments on YouTube: A Systematic Comparison regarding the Accuracy of Recurrent Neural Network and Machine Learning Models. Paper presented at the Annual Meeting of the International Communicaton Association (ICA), Converted from Australia to virtual due to COVID-19, May 2020.

Röchert, D., Neubaum, G. Ross, B., Brachten, F., & Stieglitz, S. (2019, September). Digitale Arenen auf YouTube: Untersuchung von meinungsbasierter Homogenität mit Hilfe von Sentiment- und Netzwerkanalysen. Paper to be presented "Die datafizierte Gesellschaft: Praktiken, Prozesse und Folgen der Datafizierung", Bonn, DE, September 2019.

Röchert, D., Neubaum, G. Ross, B., Brachten, F., & Stieglitz, S. (2019, May). Can You Hear the Echo? Combining Sentiment and Social Network Analyses to Measure Opinion-Based Homogeneity in Social Media. Paper presented at the Annual Meeting of the International Communicaton Association (ICA), Washington, DC, USA, May 2019.

Röchert, D., & Neubaum, G. (2019, February). The networked collusion - Examining the virality of conspiracy theories on YouTube. Paper presented at the Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM), Hamburg, DE, February 2019.

Röchert, D. (2018). Identification of opinion-based homogeneity and heterogeneity in online networks (YouTube). Paper presented at the Annual Meeting of the data2day, Heidelberg, BW, Germany, September 2018

 

Other Contributions / Reports

Neubaum, G., Cargnino, M., Röchert, D., & Maleszka, J. (2018). Gefangen im Netz der Gleichdenkenden? Eine medienpsychologische Analyse von (politischer) Homogenität in sozialen MedienPsychologie in Österreich5, 384-390.