Fiona Draxler, Professorship for Social Data Science and Methodology: Data quality in the social sciences (January 2024)

Fiona Draxler recently joined the University of Mannheim as a postdoctoral researcher, supporting the project KODAQS (Competence Center for Data Quality in the Social Sciences). She has a background in human-computer interaction and computer science. In 2023, she completed her PhD on context-aware personalized language learning technologies at LMU Munich.

What is your current research topic?

Social media, smartphones, and devices like smartwatches provide new data sources for the social sciences. For instance, interactions on online opinion platforms can offer insights into political sentiment, and (anonymized) location data can illustrate activity patterns of different socio-demographic groups. However, making valid conclusions from this data requires attention to factors like representativeness and measurement errors. I investigate how and to what extent insights from survey methodology can be applied to these newer data types to support researchers. Moreover, I research how intelligent systems and AI can foster data quality.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

I support researchers in learning more about our society from people's behavior on the internet and in everyday life.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

Methodological basis, inspiration

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

In the KODAQS project, we use a variety of methods including natural language processing for stance detection or sentiment analysis and linkage techniques for merging different data sources. We will expand this range as we increase the diversity of data types.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?

Data Science principles are essential for assuring the validity of my research.

What development opportunities do you see for the topic of Data Science in relation to your field?

In particular, recent advances in machine learning provide new research opportunities. We should assess how we can creatively and reflectively use intelligent tools for social science enquiries, at the analysis but also already at the planning stage.