I am working as a Post-Doc at GESIS – the Leibniz Institute for the Social Science in Cologne, Germany at the Computational Social Science department.

I am interested in applying data science methods, such as Machine Learning (ML) and Natural Language Processing (NLP), to research subjects, such as deception detection, fraud prevention, dark web markets, and data biases impacting ML performances.

Google Scholar, ORCID, GitHub, Twitter, LinkedIn, GESIS Profile

Brief CV

• 2021-present: Post-Doc, Department of Computational Social Science, GESIS - Leibniz Institute for the Social Science, Cologne, Germany
• 2018-2023: PhD in crime science (Thesis: Combating online consumer fraud and Counterfeits: A data science perspective), Department of Security & Crime Science, Dawes Centre for Future Crime, University College London; Supervised by Bennett Kleinberg & Shane Johnson
• 2018: Visiting Scholar at the Language and Information Technologies Group (7 months), University of Michigan, Ann Arbor, USA; Exchange during master’s program
• 2016-2018: M.Sc. (research) degree in Brain and Cognitive Science, University of Amsterdam, Netherlands
• 2014-2016: B.Sc. in Biomimetics (Bionik), University of Applied Sciences, Germany; Change of study after 2 years
• 2011-2014: B.Sc. degree in Psychology, University of Groningen, Netherlands

Some publications

(full list, see Google Scholar)

• Soldner, F., Kleinberg, B., & Johnson, S. D. (2023). Counterfeits on dark markets: a measurement between Jan-2014 and Sep-2015. Crime Science, 12(1), 1-19. Paper, Data/Code
• Soldner, F., Kleinberg, B., & Johnson, S. D. (2022). Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin. Plos one, 17(12). Paper, Data
• Soldner, F., Kleinberg, B., & Johnson, S. (2022). Trends in online consumer fraud:: A data science perspective. In A Fresh Look at Fraud (pp. 167-191). Routledge. Publication
• Soldner, F., Tanczer, L. M., Hammocks, D., Lopez-Neira, I., & Johnson, S. D. (2021). Using Machine Learning Methods to Study Technology-Facilitated Abuse: Evidence from the Analysis of UK Crimestoppers’ Text Data. In The Palgrave Handbook of Gendered Violence and Technology (pp. 481-503). Palgrave Macmillan, Cham. Paper, trained ML model
• Soldner, F., Perez-Rosas, V., & Mihalcea, R. (2019). Box of Lies: Multimodal Deception Detection in Dialogues. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 10. Paper, Data
• Soldner, F., Ho, J. C., Makhortykh, M., van der Vegt, I., Mozes, M., & Kleinberg, B. (2019). Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels. Third Workshop on Natural Language Processing and Computational Social Science, 10. Paper, Data, Code

Talks, Posters and Guest lectures

• 2023 - Invited guest lecture about anonymous networks and cryptomarkets (Technische Universität Kaiserslautern)
• 2022 - Presentation at Fifth Annual Cybercrime Conference at the Cambridge Cybercrime Centre: “From the dark to the surface web: Scouting eBay for counterfeits”
• 2021 - Presentation at IC2S2-2021: “Data confounds lead to performance overestimations in fake review detections”
• 2021 - Invited guest lecture about the sale of counterfeits on darknet markets (University of Amsterdam).
• 2021 - Invited guest lecture about confounds in data, used for automated fake review detection (UCL).
• Presentation at NAACL 2019, Workshop NLP + CSS (June, Minneapolis, USA) and Euro CSS 2019 (September, Zürich, Switzerland) about “Sentiment patterns in videos from left- and right-wing YouTube news channels.”
• Poster presentation at NAACL 2019 (June, Minneapolis, USA) about “Box of Lies: Multimodal Deception Detection in Dialogues.”

Teaching Activities

GESIS:

• Seminar co-lead (2022, 2023): Automated Web Data Collection with Python (Fall Seminar, 5 days). GESIS - Leibniz Institute for the Social Science, Mannheim.
• Workshop co-lead (2023): Collecting data with APIs: Wikipedia as an example (1 Day). University of Giessen

UCL – Department of Security and Crime Science:

• Teaching Assistant: Preventing Crimes (18/19; 19/20) – M.Sc. Module, grading class assessments and work on online portal.
• Teaching Assistant: Applied Data Science (19/20; 20/21) – M.Sc. Module, supervising coding tutorials (R), grading class assessments and work on online portal.
• Teaching Assistant: Security Technologies (19/20) – B.Sc. Module; supervising tutorials with handling security equipment and work on online portal.
• Teaching Assistant: Advanced Crime Analysis (18/19) – B.Sc. Module; supervising coding tutorials (R), grading class assessments and work on online portal.
• Master’s dissertation grading

Other Activities

• Student supervision (B.Sc. / M.Sc. Thesis, research intern)
• Peer-reviewing: Crime Science, PeerJ Computer Science, Natural Language Engineering, EMNLP 2022, 4th NLP+CSS Workshop at EMNLP,
• Mentoring (supporting first-year PhD students)
• Summer School “The Sleeping Brain: From Neural Networks to Cognition”, 2017 University of Amsterdam; Lectures and practical work on Implanting new memories during short naps, Poster presentation