LLM Power to the People ✊
Teachers
- Étienne Ollion, Professor of Sociology, Ecole Polytechnique, Paris, France
- Émilien Schultz, Senior Data Scientist, CREST-Institut Polytechnique de Paris, France
Description
This tutorial aims to provide an up-to-date overview of the applications of large language models (LLMs) in research, with a particular focus on key areas such as fine-grained text classification, information extraction, and text clustering. To this end, it will cover fundamental concepts, including zero-shot learning, fine-tuning, encoder-decoder architectures, and Low-Rank Adaptation (LoRA), while also presenting various types of language models and their respective affordances. Drawing on the most recent discussions in the field, the tutorial will offer guidance on developing efficient processing pipelines, taking into consideration the computational resources available to researchers. Participants will gain practical knowledge through a combination of theoretical discussions and hands-on case studies.
Bridging Human and LLM Annotations for Statistically Valid Computational Social Science
Teachers
- Kristina Gligorić, Postdoctoral Scholar, Computer Science, Stanford University
- Cinoo Lee, Postdoctoral Scholar, Psychology, Stanford University
- Tijana Zrnic, Ram and Vijay Shriram Postdoctoral Fellow, Stanford Data Science, Stanford University
Description
The tutorial provides participants with a practical, hands-on experience in integrating Large Language Models (LLMs) and human annotations to streamline annotation workflows, ensuring both efficiency and statistical rigor.
The Role of AI in Misinformation: Current Trends, Detection, and Mitigation
Teachers
- Miriam Schirmer, Postdoctoral Scholar, Northwestern University
- Julia Mendelsohn, Postdoctoral Scholar, University of Chicago
- Dustin Wright, Postdoctoral Fellow, University of Copenhagen
- Dietram A. Scheufele, Taylor-Bascom Chair and Vilas Distinguished Achievement Professor, University of Wisconsin-Madison
- Ágnes Horvát, Associate Professor of Communication and Computer Science, Northwestern University
Description
As AI-generated content becomes more prevalent, understanding its role within the broader misinformation landscape is critical. The widespread proliferation of misinformation in combination with the rise of AI technologies poses challenges across domains.
Planetary Causal Inference: an R tutorial on how to conduct causal inference with satellite images data
Teachers
- Adel Daoud, Associate Professor at Institute for Analytical Sociology, Linköping University
- Connor Jerzak, Assistant Professor in Government, UT Austin
Description
This R tutorial is based on our book-in-progress, Planetary Causal Inference (PCI), which proposes using Earth observation (EO) data to enhance social science research by expanding both the scope and resolution of data analysis.
A Workflow for Open Reproducible Computational Social Science
Teachers
- Caspar van Lissa, Associate Professor, Tilburg University, Tilburg, Netherlands
Description
Reproducibility is essential for establishing trust and maximizing reusability of empirically calibrated simulations and other computational social science studies. Participants learn to make research projects open and reproducible according to the FAIR principles and TOP-guidelines.
Research Cartography with Atlas
Teachers
- Mark Whiting, CTO, Pareto Inc. and visiting scientist at University of Pennsylvania
- Linnea Gandhi, Lecturer and PhD candidate at Wharton at University of Pennsylvania
- Amirhossein Nakhaei, M.Sc. Computational Social Science, RWTH Aachen
- Duncan Watts, Stevens University Professor at University of Pennsylvania
Description
Scientific inquiry depends on "standing on the shoulders of giants" — building on the findings of prior work. However integrating knowledge across many papers is challenging and unreliable. Atlas, an open source platform, tackles this problem by emphasizing commensurability.
Scalable Analysis of GPS Human Mobility Data with Applications to Socio-Spatial Inequality
Teachers
- Jorge Barreras, Postdoc, University of Pennsylvania; Computational Social Science Lab (CSSLab), Wharton School
- Thomas Li, M.Sc. Student, School of Engineering, University of Pennsylvania
- Chen Zhong, Associate Professor in Urban Analytics, Centre for Advanced Spatial Analysis (CASA), UCL
- Cate Heine, Research Fellow in Urban mobility and inequality, CASA UCL
- Adam (Zhengzi) Zhou, PhD student at CASA UCL
Description
Large-scale human mobility datasets derived from mobile phones have become a valuable resource in the field of human mobility. They have found diverse applications in tasks such as travel demand estimation, urban planning, epidemic modelling, and more.
Mobility Flows and Accessibility Using R and Big Open Data
Teachers
- Egor Kotov, PhD Student, Max Planck Institute for Demographic Research, Rostock, Germany
- Johannes Mast, PhD Student, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR)
Description
Large-scale human mobility datasets provide unprecedented opportunities to analyze movement patterns, generating critical insights for many fields of research. This workshop addresses challenges by showcasing end-to-end workflows that harness state-of-the-art R packages and methods.
Reinforcement Learning and Evolutionary Game Theory are Two Sides of the Same Coin
Teachers
- Paolo Turrini, Associate Professor, Department of Computer Science, University of Warwick, UK
- Elias Fernández Domingos, Postdoctoral Researcher at the AI Lab, Vrije Universiteit Brussel, Belgium
Description
Assuming that individuals are rational is often unjustified in many social and biological systems, even for simple pairwise interactions. This tutorial shows how evolutionary game theory and multi-agent reinforcement learning, although applied in different contexts, are two sides of the same coin.
Computational Social Science for Sustainability
Teachers
- Matthew A. Turner, Lecturer in Environmental Social Sciences at the Stanford Doerr School of Sustainability, Stanford University
- James Holland Jones, Professor, Environmental Social Sciences, Stanford Doerr School of Sustainability, Stanford University
Description
Humans face an existential challenge to transition to sustainable practices that do not exhaust available ecological, economic, and social capital. Computational social-cognitive models can be used to deduce the efficacy of potential training or educational interventions to promote sustainable practices.
Making Models We Can Understand: An Interactive Introduction to Interpretable Machine Learning
Teachers
- Chudi Zhong
- Alina Jade Barnett
- Harsh Parikh
Description
In many areas of social science, we would like to use machine learning models to make better decisions. However, many machine learning models are opaque or "black-box." Interpretable machine learning models give insight into model decisions and can be used to create more fair and accurate models.
New Approaches and Data Sources to Study Digital Media and Democracy
Teachers
- Sebastian Stier
- Philipp Lorenz-Spreen
- Lisa Oswald
- David Lazer
Description
As we head into a crucial election year, political forces and societal processes such as polarization or declining trust pose threats to the legitimacy of democratic institutions. This workshop aims to bring together research groups working on new technical solutions and innovative approaches for studying digital democracy.
Exploring Emerging Social Media: Acquiring, Processing, and Visualizing Data with Python and OSoMe Web Tools
Teachers
- Filipi Nascimento Silva
- Kaicheng Yang
- Bao Tran Truong
- Wanying Zhao
Description
In the digital age, social media platforms have become crucial for societal interaction and communication. This tutorial aims to guide participants through new developments, highlighting the current approaches for accessing social media data and methodologies to understand this data.
Collecting Digital Trace Data Through Data Donation
Teachers
- Laura Boeschoten
- Niek de Schipper
Description
This tutorial helps IC2S2 researchers understand and deploy an alternative to circumvent data access challenges. This alternative approach to gain access to digital traces is enabled thanks to the GDPR's right to data access and data portability and similar legislation.
Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers
Teachers
- Jae Yeon Kim
- Tiago Ventura
- Aniket Kesari
- Sono Shah
- Tina Law
- Subhik Barari
- Sarah Shugars
Description
Social scientists with data science skills are increasingly assuming positions as computational social scientists. We provide an accessible tutorial for CSS training based on our collective working experiences in academic, public, and private sector organizations.
Using LLMs for Computational Social Science
Teachers
- Diyi Yang
- Caleb Ziems
- Niklas Stoehr
Description
Our tutorial will guide participants through the practical aspects and hands-on experiences of using Large Language Models (LLMs) in Computational Social Science (CSS). In recent years, LLMs have emerged as powerful tools capable of executing a variety of language processing tasks in a zero-shot manner.
Thinking With Deep Learning: An Exposition Of Deep (Representation) Learning for Social Science Research
Teachers
- James Evans
- Bhargav Srinivasa Desikan
Description
A deluge of digital content is generated daily by web-based platforms and sensors. Emerging deep learning methods enable the integration and analysis of these complex data in order to address research and real-world problems.
Active Agents: An Active Inference Approach to Agent-Based Modeling in the Social Sciences
Teachers
- Andrew Pashea
Description
This tutorial will teach attendees about Active Inference as an agent-based modeling framework and its application to computational social science. Active Inference is an integration of neuroscience and cognitive science which builds a normative theory for biological and social phenomena.
The Dark Web: Harnessing the Platform for Social Science Research
Teachers
- Brady Lund
Description
The dark web remains mysterious, with many struggling to comprehend its nature. This workshop aims to provide participants with an understanding of the dark web—its functioning and how to access it, along with the ethical and legal considerations surrounding it.