Lecture series: Computational Social Science

This session introduces students and guest listeners to the contents of the lecture series. Main questions are: What is Computational Social Science? What is the SOUNDS project? How will the lecture series proceed?

Presenters: Dr. Martin Ulrich  (SOUNDS)

This session presents novel data sources that can be used for Social Sciences thanks to its collaboration with informatics. These novel data sources range from social media advertising data to satellite imagery.

Presenters:  Prof. Daniela Braun  (Universität des Saarlandes, I2SC & SOUNDS) &  Prof. Ingmar Weber  (Saarland University, I2SC, SOUNDS)

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Presenter: Ass.Prof. Joseph Aylett-Bullock  (University of Bristol)

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Presenter: Dr. Leah von der Heyde (GESIS Leibniz-Institute for the Social Sciences)

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Presenter: Prof. Dongwon Lee (Pennsylvania State University)

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Presenter: Prof. Zoë Greene (University of Strathclyde, Glasgow)

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Presenter: Dr. Pola Lehmann  (Berlin Social Science Center, WZB)

International crises and Euroscepticism have made European issues prominent in citizens’ lives. The article presented studies the role of three key European issues – migration, the environment, and EU integration – for political parties and citizens. The analysis centres on nine EU member states, combining party manifestos from the 2024 European Parliament elections with survey data. It finds a gap between the concerns of citizens and the political parties, an important consideration for election campaigns in general. Moreover, the analysis suggests that the salience in party manifestos has a modest direct influence and a stronger indirect impact on their appeal to citizens in most countries studied. Specifically, citizens concerned with migration and the environment evaluate parties based on the prominence they give to these issues during the campaign. The findings offer important avenues for further research on party issue emphasis and the measurement of issue salience via large language models (LLMs).

Presenter: Dr. Alex Hartland (Saarland University)

Digital technologies and large-scale data are transforming urban governance - so promises the smart city. GIS-based planning tools and open data platforms suggest that cities are becoming ever more data-driven. Yet the everyday reality of urban planners tells a more ambivalent story. Drawing on qualitative workplace interviews with planners in small and medium-sized cities in Germany, Austria and Switzerland, this lecture presents research on how digital transformation is actually embedded - and obstructed - in planning practice. We show that planners navigate between innovative ambitions and erratic everyday routines: ICTs become meaningful only when anchored in local knowledge practices, while limited resources, hierarchical procedures and incomplete digitisation produce persistent frictions. In a second step, we ask what Computational Social Science can contribute to understanding urban planning and governance. We discuss the potential of text-based methods (e.g. analysis of planning documents, public consultation records, or social media debates about urban development), spatial data approaches (GIS, remote sensing, urban mobility data), and participatory data sources for studying planning processes at scale - and reflect on where computational approaches reach their limits and where qualitative, practice-oriented research remains indispensable.

Presenter: Jun.-Prof. Carola Fricke (Saarland University)

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Presenter: Brahmani Nutakki (Saarland University, I2SC)

Prediction markets have emerged as a powerful source of real-time information about expectations of future events. In this lecture, we will first introduce the foundations of prediction markets, explain how they work, and discuss why they have become an increasingly valuable data source for computational social science and machine learning. We will then present our ongoing research on modeling the evolving state of the world using prediction market time series, including methods for identifying latent relationships between events, detecting market-wide anomalies, and uncovering potentially causal dependencies across interconnected markets. Finally, we will discuss how large language models can be used to characterize news and media sources by their influence on future market predictions, providing new perspectives on information diffusion, forecasting, and the interaction between media, human expectations, and AI. Overall, the lecture will highlight how prediction markets provide a unique setting for studying collective intelligence, forecasting, and AI, while outlining several open research challenges at the intersection of computational social science, economics, and machine learning.

Presenters: Prof. Krishna Gummadi  (Max-Planck-Institut for Software Systems), Prof. Marius Kloft  (RPTU Kaiserslautern-Landau), Dr. Johnnatan Messias  (Max-Planck-Institut for Software Systems)

Google Maps is used by billions of people every day — but beyond navigation, it holds a rich trove of data about how cities look, how businesses thrive or disappear, and how people experience public space. In this session, we explore how Google Maps data (street view imagery, place reviews, Points of Interest, and more) can serve as a novel data source for computational social science research. We discuss the unique advantages and limitations of this data, show how it can be combined with other datasets, and present concrete research examples that use it.

Presenter: Ethel Elikem Afi Mensah (Saarland University, I2SC)

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Presenter: Prof. Vera Schmitt  (JGU Mainz)

Digital platforms have become central arenas of public life, where societal challenges (from climate change and migration to inequality and public health) are debated and shaped. To examine discourses, dynamics, and impacts across multilingual, multi-context digital publics, social scientists increasingly rely on AI-driven computational methods. This talk introduces methodological innovations in digital methods, focusing on computational multilingual text analysis and the problem of measurement validity. It present a validation framework that accounts for context-specific biases in automated measurement and demonstrate how it improves the robustness and comparability of findings across languages and platforms. In the second part, it showcases empirical studies that deploy the some of the tools to analyze public discourse around environmental and inequality-related policies, highlighting what AI can, and cannot, capture about actors, topics, and diffusion dynamics. The talk close with implications for research design, and the responsible use of AI in social science research.

Presenter: Dr. Fabienne Lind

On this date, the students will take their written exam.