People
SODAS steering committee
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Morten Axel Pedersen is Professor of Anthropology and the Director of the Copenhagen Center for Social Data Science (SODAS), University of Copenhagen. An anthropologist by training and the author of several ethnographic monographs (most recently Collaborative Damage: An Experimental Ethnography of Chinese Globalization), he has for soon ten years been involved in the development and institutionalization of social data science as a distinct research field and as a graduate degree program. Since 2020, he has been principal investigator of the ERC Advanced Grant funded project DISTRACT: The Political Economy of Distraction in Digitized Denmark. Social Data Science Skills: Quali-Quantitative Methods, Data Ethics, Social Data Theory, Computational Anthropology SODAS projects: Critical Algorithm Lab (CALL), DISTRACT, The Dynamics of Political Discourse and Attention during the COVID-19 outbreak, COVID-19 Snapshot MOnitoring in Denmark (COSMO Denmark) |
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Social Data Science Skills: Online experiments and surveys, psychology of online behavior, data ethics SODAS projects: COVID-19 Snapshot Monitoring in Denmark (COSMO Denmark) |
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Social Data Science Skills: Digital political science, practice and affordance methodologies, the relationship between off-line and online worlds, digital mis- and disinformation. SODAS projects: DIPLOFACE, Digital Disinformation, HOPE |
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Roberta Sinatra is Professor in Data Science at SODAS. She also holds visiting positions at IT University of Copenhagen, where she is part of the the Networks, Data, and Society (NERDS) Research group, at the Complexity Science Hub (Austria), and at ISI Foundation (Italy). Her research is at the forefront of network science, data science, and computational social science. She has been awarded with a Sapere Aude grant and a Villum Young Investigator grant to study quantitatively bias in measures and algorithms of scientific impact. SODAS projects: Bias Explained: Pushing Algorithmic Fairness with Models and Experiments Social Data Science Skills: Network analysis, Complex systems, Data Analysis, Algorithmic Bias. |
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Social Data Science Skills: Machine Learning, Data Analysis, Network analysis, Complex systems SODAS projects: Microdynamics of Influence in Social Systems, Social Fabric, Nation-scale social networks, HOPE |
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Social Data Science Skills: Digital methods, Quali-quantitative methodologies, Science & technology studies (STS), Data infrastructures and politics SODAS projects: SODAS Climate, Critical Algorithm Lab (CALL), DISTRACT, Risk Attention Ecologies |
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Social Data Science Skills: Machine learning in econometrics, Network modelling, Spatial data SODAS projects: Social Fabric, Nation-scale social networks |
Andreas Bjerre-NielsenProfessors
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Social Data Science Skills: Combining big data and register data, data law and ethics, machine learning in the social sciences, quasi-experiments in social big data SODAS projects: Mass Politics and Social Media, Social Fabric, Nation-scale social networks david.dreyer.lassen@sodas.ku.dk |
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Social Data Science Skills: surveys and experiments, economics and psychology of online behavior and human-computer interaction SODAS projects: COVID-19 Snapshot MOnitoring in Denmark (COSMO Denmark) |
Robert BöhmAssociate Professors
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Social Data Science Skills: Text as data, Combining survey and register data, Twitter data, Machine learning in the social sciences SODAS projects: What do the Danes think, know and mean about the Corona Epidemic?, The Dynamics of Political Discourse and Attention during the COVID-19 outbreak |
Hjalmar Alexander Bang Carlsen is an Associate Professor at SODAS. He works in the intersection between social data science, political sociology and pragmatism. He has 2 main projects 1) activists patterns of engagement 2) methodological issues within quantitative and qualitative text analysis, and especially their combination. Social Data Science Skills: Text Methodology, Digital Mixed Methods, Interactionism, Social Media SODAS projects: Solidarity and Volunteering in the Corona Crisis, The Dynamics of Political Discourse and Attention during the COVID-19 outbreak, COVID-19 Snapshot MOnitoring in Denmark (COSMO Denmark) |
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Social Data Science Skills: Digital ethnography, digital methods, text as data, data ethics, GDPR SODAS projects: Data Governance after GDPR, DISTRACT, Risk Attention Ecologies |
Assistant Professors
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Clara Vandeweerdt is an Assistant Professor at SODAS and the Department of Political Science. Her research areas are political communication and political psychology, with a focus on on climate change. In particular, she studies how social identities inform people's political opinions. She has also used machine learning to understand how political talk radio content reacts to major events, and natural language processing methods to analyze the identities of people on Twitter. Social Data Science Skills: causal identification, machine learning/natural language processing, Bayesian statistics, web scraping |
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Social Data Science Skills: Social network analysis, Text analysis, Mix methods, Russian social media |
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Social Data Science Skills: Online experiments and surveys, data analysis, machine learning. SODAS projects: COVID-19 Snapshot Monitoring in Denmark (COSMO Denmark). |
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Josefine Bohr Brask is an assistant professor at SODAS and affiliated with the Section for Ecology and Evolution at the Department of Biology. Her research connects social behaviour, network science, behavioural ecology and complex systems, with social networks as a central theme. She is interested both in fundamental questions about sociality and theoretical aspects of networks. Her research includes modelling, data-based studies, and methods development. Social data science skills: network modelling, network analysis, complex systems, animal social systems, game theory SODAS projects: New computational approaches for understanding the emergence of social network structures Social media: @JBBrask |
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Stephanie Brandl is an Assistant Professor at SODAS. She has a background in Natural language processing and Machine learning. Her research focuses on explainability and fairness in NLP as well as applying NLP to research questions from the social science. She is interested in increasing the trustworthiness of language models, i.e., making them fair and transparent, and making models accessible to research outside of computer science. Social data science skills: Machine learning, natural language processing, algorithmic bias/fairness |
Postdoctoral Researchers
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Social Data Science Skills: Digital International Relations, ethnographic and interpretive analysis of digital data, practice methodologies SODAS projects: DIPLOFACE |
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He serves as a member of The Lancet Countdown on Health and Climate Change’s Working Group 1, and is developing social media-based indicators to monitor expressed emotional responses to local climate extremes across nearly every county globally. Kelton received a Ph.D. in Planetary Social and Behavioral Data Science at SODAS and was a visiting researcher at the University of California Berkeley’s Global Policy Laboratory. He previously was a US-Denmark Fulbright grant recipient and received his M.S. in Human Environment Relations from Cornell University. |
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Adéla Plechatá is a postdoctoral researcher at SODAS. Her research focuses on understanding and promoting prosocial behavior, particularly in the contexts of climate action and health. She employs innovative approaches, such as virtual reality, to explore and encourage prosocial behavior at both individual and collective levels. Currently, her work at SODAS is centered on investigating the nature of curiosity in both children and adults. Social Data Science Skills: surveys and experiments, causal inference, data analysis, field experiments SODAS projects: Shedding New Light on Curiosity in Children and Adults |
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Claudia Acciai is a post-doctoral researcher at ITU, where she is affiliated with NERDS. Her work lies at the intersection of comparative public policy, innovation studies, and sociology of science. In her research, she combines computational and experimental methods with qualitative content analysis techniques. She has been working on quantifying institutional and country-related Matthew effects in science and on scientific misinformation. She is currently part of a Villum Synergy research project investigating trends in the usage and prevalence of LLMs across scientific disciplines. Social Data Science Skills: surveys and field experiments, large-scale data analysis, causal inference. |
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Snorre Ralund is a postdoctural researcher at SODAS. His primary research interests lie in the field of Applied Natural Language Processing, with a particular focus on bridging the gap between model development and practical, real-world measurement. This work encompasses investigating model biases, designing robust validation techniques, and developing innovative sampling methods to ensure the reliability and fairness of NLP systems. More broadly, his work focuses on leveraging computational tools to support the creation of grounded, meaningful, and reliable categorization. His research is centered around online sources (social media and website crawls), but on the fringes I experiment with among other things image and sound data. |
Abbas K. Rizi is a postdoc researcher at DTU Compute, working on the Nation Scale Project. His work focuses on spreading patterns, diffusion processes, and epidemic interventions. He is particularly interested in understanding social behavior to predict life outcomes and bridging the theory and practice gap. He began his career in physics but ended up earning a Doctor of Science (D.Sc.) degree from Aalto University's Computer Science Department. Alongside his research, Abbas actively contributes to public science communication as a science writer and serves as the editor-in-chief of sitpor.org (FA).
PhD Students
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Social Data Science Skills: Images as data, visual methods, cultural analytics, digital network analysis, computer vision. SODAS projects: DISTRACT, DIPLOFACE, Digital Disinformation, |
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Social Data Science Skills: Natural language processing, large scale data analysis, cognitive science, ethnography, and computational social science SODAS projects: DISTRACT |
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Social Data Science Skills: Machine learning, statistical modelling, causal inference and matching algorithms. |
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Magnus Lindgaard Nielsen is a PhD student at SODAS. He holds a BSc in Economics and is currently finishing his Master’s degree in Economics alongside his PhD studies. His research focuses on explaining how our social networks influence a diverse range of life outcomes utilizing registry data and a combination of predictive modelling and causal inference using natural experiments. The PhD forms part of the project Nation-scale social networks. Social Data Science Skills: Machine learning, microeconometrics, computational social science, causal inference. SODAS projects: Nation-scale social networks |
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Social Data Science Skills: Machine learning, microeconometrics, network science, sensor data analysis, computational social science. SODAS projects: Nation-scale social networks |
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Social Data Science Skills: ethnography, netnography, digital methods, STS, problematization analysis SODAS project: Risk Attention Ecologies |
Sofie Læbo Astrupgaard is a PhD student at SODAS. She has a BSc in Anthropology, and is currently finishing her Master’s degree in Social Data Science alongside her PhD studies. Sofie’s research focuses on practices and regulations related to use of digital devices in workplaces and schools. Her PhD is part of the ERC-funded project DISTRACT: The Political Economy of Attention in Digitized Denmark, where she is part of Subproject 4 – Regulating Distraction. Social Data Science Skills: Natural language processing, digital network analyses, large scale data analysis, ethnography, development of computational ethnographic approaches. SODAS project: DISTRACT |
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Ella Jacobsen is a Ph.D. student at the math department of the University of Copenhagen in collaboration with SODAS. She holds a M.Sc. in Physics from the University of Copenhagen. At SODAS, she is affiliated with the nation-scale project, with the goal of applying causal inference techniques on Danish registry data. Social Data Science skills: machine learning, scraping, causal inference techniques. |
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Marilena Hohmann is a PhD Student in Social Data Science at SODAS and the University of Edinburgh. Drawing on a combination of network science methods and political science theory, she studies opinion dynamics and political polarization in social networks. Her PhD project quantitatively investigates ideological and affective aspects of political polarization using large-scale social media data. Marilena holds an MSc in Social Data Science (University of Copenhagen), and a BA in Media and Communication Studies (Freie Universität Berlin and Universidad Carlos III de Madrid). Social Data Science Skills: quantitative network analysis, large-scale data analysis, social media data, computational communication studies |
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Zoe Horlacher is a PhD student at SODAS. She holds an MSc in Cognitive and Decision Sciences from University College London and an MA (Honours) in Sustainable Development from the University of St Andrews. Her main research interests lie at the intersection of environmental psychological theory and social data science. Zoe is specifically interested in using natural language processing to create data-driven insights into cross-cultural sustainability perceptions, as well as pro-environmental personality traits and emotions. Social Data Science skills: Data science for environmental psychology, natural language processing, cognitive science, quali-quantitative methodology. |
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Social Data Science skills: Machine learning, statistics, causal inference and visualization. |
Jacob Aarup Dalsgaard is a Phd student at SODAS. He holds a BSc in Cognitive Science and Mathematics from Aarhus University and an MSc in Data Science from the IT University of Copenhagen. His research focusses on algorithmic fairness in a Science of Science context and aims to develop new models to understand inequality and improve the fairness of algorithms through de-biased impact measures. SODAS projects: BiasExplained: Pushing Algorithmic Fairness with Models and Experiments Social Data Science Skills: Algorithmic Fairness, Recommender Systems, Network Science, Information Retrieval, Deep Learning. |
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Marten Appel is a PhD student in Social Data Science at SODAS, specializing in understanding what factors drive and predict human cooperation. He holds an M.Sc. in Political Science from Aarhus University. His current research focuses on predicting human cooperation within economic games and assessing whether our measures of cooperation reflect real-world behavior. His PhD project contributes to the broader project 'Understanding Human Behavior: From Prediction to Theory'.
Social Data Science Skills: Machine learning, Laboratory and Online Experiments, Web scraping. |
Anders Weile Larsen is a visiting PhD from ITU, working with Roberta Sinatra (KU, SODAS) and Vedran Sekara (ITU, NERDS) on a project which aims to uncover the fundamental limits for predicting human behavior and social systems. He holds a BSc in Cognitive Science and Sociology from Aarhus University and a a MSc in Social Data Science from the University of Copenhagen. Anders’ PhD is funded by the Danish Pioneer Centre for AI, where he is also affiliated. Social Data Science Skills: Machine Learning, Network Science, Information Theory. |
Esther Chemnitz is a visiting PhD student from Political Science at Aarhus University where she is a part of Michael Bang Petersen's ROPH project (Research on Online Political Hostility). She holds a BA in Film and Media Studies and an MA in Cognition and Communication. Her research combines media theories of context collapse and cognitive theories of language-use. In her dissertation she asks how social media users' assumptions about the political beliefs of their online audience affect the way they tailor their utterances. She also studies the potentially polarizing effect of political utterances being formulated for one audience but received by another. |
Cecilie Fenja Strandsbjerg is a PhD student at SODAS. She holds a master’s degree in psychology from UCPH. Her research interests lie in personality, environmental psychology, and person-situation interplay. Her PhD project focuses on curiosity in adults, aiming to develop novel measurement tools, investigate the role of curiosity in various life outcomes, and foster curiosity through interventions. The PhD project is funded by the Tryg Foundation. Social Data Science skills: Online experiments and surveys, data analysis, combining survey and register data. |
Iva Vukojević is a visiting PhD student of psychology from the University of Zagreb, where she partakes in an interdisciplinary research project entitled "Computational Models for Text-Based Personality Prediction and Analysis." Her research centres around studying human behaviour based on textual footprint, with an emphasis on personality and its related constructs. Her PhD topic is on stylistic textual signals reflecting the adaptive mechanisms of maintaining a positive self-view. Social Data Science Skills: text as data, natural language processing, online experiments and surveys, data analysis. |
Aaron Pope is a PhD student at SODAS. His dissertation studies whether TikTok's unique features - as a video-based and algorithmically reliant platform - contribute to political polarization amongst its users. Previously he was a researcher at New York University's Center for Social Media and Politics (CSMaP), where he is still a Graduate Research Affiliate. While at CSMaP, he managed the center's Bilingual Election Monitor, a project that paired survey and digital trace data to study online media consumption and belief in misinformation among U.S.-based Latinos. He holds a Master of Public Administration from NYU and a Bachelor of the Arts in literary studies from The New School. Social Data Science Skills: Casual inference, natural language processing, computer vision, pairing survey and digital trace data, online experiments and surveys, and web scraping. |
Christian Vestergaard Djurhuus is a PhD student at SODAS, funded by the Pioneer Center for Artificial Intelligence (P1). His research focuses on modeling human life trajectories using registry data and graph representation learning techniques emphasizing how social networks influence life outcomes. He holds a BSc in Artificial Intelligence and Data and a MSc in Human-Centered Artificial Intelligence from DTU. Social Data Science Skills: Machine learning, deep learning, graph representation learning, natural language processing, network analysis, time-series analysis. SODAS projects: Nation-scale social networks |
Tereza Blažková is a PhD student in Social Data Science at the University of Copenhagen. She holds a Bachelor’s degree in Sociology with Economics and a Master’s degree in Social Data Science. Her research focuses on algorithm audits and their application to predictive analytics in education. Her academic interests revolve around responsible AI, algorithmic fairness, algorithm auditing, data governance, and education data science. Social data science skills: Algorithmic fairness, computational social science, data analysis, discourse analysis, machine learning, and natural language processing. |
Elisabetta Salvai is a PhD student at SODAS affiliated with the NEtwoRks, Data and Society group of the IT University, founded by the Pioneer Center for Artificial Intelligence P1. She holds an MSc in Physics of Complex Systems from the University of Turin, Italy. Her work centers around applying complex systems methods to uncover and explore bias existing in data and machine learning algorithms. She is currently working on the study of fairness in rankings of networks with binary attributes. Social data science skills: Data analysis, network science methods and analysis, machine learning techniques, computational modeling of dynamical processes. |
Daniel Juhász Vigild is a PhD student as SODAS and ROCKWOOL Foundation Research Unit. He holds a Msc. in Business Analytics from DTU.
His research examines the digitalization of the public sector, with a focus on quantifying the productivity enhancements and potential social costs of implementing digital initiatives.
He is currently examining the effect of implementing POL-INTEL, an intelligence-led policing tool implemented in Denmark in 2018.
Vincent Holler Gadegaard is a PhD student at SODAS. He holds a MSc in Sociology from the University of Copenhagen. In his research, he is concerned with gender differences in political deliberative participation on social media, with the aim to develop new methodologies to researching interaction and gender in political deliberation on social media.Data Lab
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Social data science skills: Data management and long-term archival, data privacy and ethics, online surveys, polling, and non-probability sampling. |
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Yani Kartalis, is a postdoctoral researcher at the Copenhagen Center for Social Data Science (SODAS) and a Data Steward at the Center’s Social Science Data Lab. His research interests lie in the fields of representation, parliaments, political parties, and data social science more broadly. He uses a multitude of parliamentary data to examine the ways representatives use parliamentary tools to represent their constituents. In the DataLab he is responsible for building social data science research capacity at the Faculty of Social Sciences and foster better data management practices. His tasks include assisting the faculty with various individual research tasks, from data mining to analysis, as well as facilitating collaboration among faculty members. Social Data Science Skills: Text-as-Data, Natural Language Processing, Data mining (Web Scraping, OCR) and analysis (Quantitative methods, Modelling) |
Siri Völker is a Data Manager at the Social Sciences Data Lab. She has a background in social sciences research and research data management, and assists the researchers at the Faculty of Social Sciences in managing quantitative as well as qualitative research data. Outside of research data management (RDM) support, she is interested in social movements and critical data studies. |
Joseph Burgess is a Data Steward at SODAS and the Team Leader at the Social Science Data Lab, which assists faculty in data management and compliance. In his research support work, he prioritizes infrastructure for storage of research projects and datasets, setting up data management agreements, compliance, and archival. Outside of research support, he is interested in the intersection of election administration and social data science.
Research Assistants
Emilie Munch-Gregersen, M.Sc. in Social Data Science from KU, is a research assistant at SODAS with a background in anthropology and social data science. Her work centers around applying computational methods to ethnographic text data, and she is currently coordinating the development and testing of EthNote Beta, a new digital tool for the collection, sharing, and processing of qualitative data. Social data science skills: Ethnographic methods, digital methods, data analysis, natural language processing, network science methods. |
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Tobias Gårdhus, MSc in Sociology from KU, is a research assistant at SODAS with a background in sociology and a focus on applying digital methods within computational social science. His work centers on examining political communication and the dynamics of public attention, often through the lens of social data gathered from various online platforms. His interests extend to enhancing social science research through software development, and he is currently working on expanding the social science toolkit with large language models. Social data science skills: Data analysis, digital methods, web scraping, web development. |
 Jonas Skjold Raaschou-Pedersen, M.Sc. in Economics, KU. |
Asger Hans Thomsen holds an MSc in Sociology from the University of Copenhagen. He specializes in digital and quantitative methodologies, such as network science and natural language processing. His expertise spans various data forms and projects, including analyzing the impact of digital density on crime rates, studying peer effects on well-being, scraping data from various platforms, and analyzing and gathering biometric data. Currently, he works as a research assistant on ClimAct, mapping the climate debate in Denmark through various data science techniques and data sources. Social data science skills: Data gathering, network science, digital methods. |
Administration
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Nasim Hezaveh is the Center Administrator at SODAS. PhD in Animal Biosystematics (Zoology). |
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Karoline Husbond Andersen is Research and PhD Course Coordinator. M.Sc. in AnthropologyStudent Assistants
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Sofia Tang Nussbaumer, Master student in Psychology, KU. |
Asger Balsby Kromand, Master student in Social Data Science, KU. |
Malte Ro Buchwald, student assistant on Nation Scale, Bachelor Student in Machine Learning and Datascience, DIKU, Master in philosophy from KU. |
Ró Holm Magnadóttir, Bachelor student in Anthropology, KU. |
Moritz Johanning, Master student in Social Data Science, KU. Student Assistant at Nation-scale social networks. |
Martha Nybroe, Bachelor Student in Mathematics and Technology, DTU. Student Assistant at Nation-scale social networks. |
Jonas Paszskowiak Bacci, Master student in IT & Cognition, KU |
Olivia Sommer Droob, Bachelor Student in Mathematics and Technology, DTU. Student assistant at DISTRACT |
Frederik Winther Barkholt, Master student in Social Data Science, KU.
Interns
No current interns.
SODAS Fellows
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keywords: Human Mobility, Blockchain-based ecosystems, Network Science, Spatio-temporal data |
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Social Data Science Skills: Online experiments, statistical disclosure control, text as data, web scraping (browser automation and APIs). SODAS projects: The Dynamics of Political Discourse and Attention during the COVID-19 outbreak, COVID-19 Snapshot Monitoring in Denmark (COSMO Denmark) |
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Social Data Science Skills: Feminist technology studies, Data ethics, Ethnography of computing cultures, Human computer interaction. SODAS projects: The Dynamics of Political Discourse and Attention during the COVID-19 outbreak samantha.breslin@anthro.ku.dk |
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Social data science skills: Causal inference, Administrative data, Machine learning in the social sciences |
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Anna Sapienza is an Assistant Professor at the department of Science, Technology and Innovation (DISIT) of the University of Eastern Piedmont in Italy. She holds a PhD in Applied Mathematics from the Polytechnic University of Turin, Italy. Her research is focused on modelling human behaviour in online environments, e.g., online social networks, mobile applications, and games, via high-dimensional digital traces. She collaborates with researchers at the Center for Social Data Science (SODAS) of the University of Copenhagen, and at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark, studying smartphone usage and characteristics leading to user engagement. Her research interests stay at the intersection between computational social science, data science, and complex systems. Social Data Science Skills: Online Social Behaviour, Machine Learning, Data Analysis, Social Network Analysis |
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Luca Maria Aiello is an Associate Professor of Data Science at the IT University of Copenhagen, where he is member of the NEtwoRks, Data and Society group (NERDS). Previously, he worked for 10 years as a Research Scientist at Yahoo Labs and Nokia Bell Labs. He conducts research in Computational Social Science with a special focus on understanding cooperation processes through the study of conversational language with the use of advanced Natural Language Processing methods as analytics or assistive tools. His work has been covered by hundreds of articles published by news outlets worldwide including Wired, WSJ, and BBC. He was once interviewed by Captain James T. Kirk. |
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Sandro Sousa is a Complexity and network Scientist who focuses on socioeconomic inequalities, population dynamics and sociospatial complexity. His work integrates large & spatial data analysis, network modelling, and controlled experiments to explore how collective behaviours and inequalities emerge and evolve across physical and digital systems. Social Data Science Skills: Complexity science, network modelling & analysis, large & spatial data, randomised trials, text as data, web scrapping. |
Laura Alessandretti
Nikolaj Harmon is Associate Professor of Economics at the University of Copenhagen. He studies a wide range of labor market topics, including job search, gender inequality and selection into politics and public service. His research combines novel data sources with state-of-the art causal inference methods and machine learning.
