Petter Törnberg, an Assistant Professor in Computational Social Science, discusses findings from his research papers on the performance of Chat GPT in interpreting political tweets, the ease of using language models in social science research, and the controversy surrounding large language models in social science.
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Quick takeaways
Large language models, like Chat GPT, can outperform human experts and mechanical Turkers in annotating political Twitter messages, demonstrating their power in social science research.
Computational social science combines computational methods with social scientific theory, aiming to not only predict human behavior but also interpret and critique it.
Deep dives
The Power and Potential of Large Language Models
Large language models, like Chat GPT, are being applied in various innovative ways across different fields. They have the ability to gather and analyze data for academic research, making it easier and more accessible. This raises questions about the necessity of human subjects in research. In an exploration of whether human experts, mechanical Turkers, or Chat GPT perform better in a specific test, Chat GPT actually outperformed both human experts and Turkers. This demonstrates the power and potential of large language models in social science research.
Computational Social Science as an Emerging Discipline
Computational social science is an emerging discipline that combines computational methods with social scientific theory. It is still in the process of development and there are different perspectives within the field. Some focus solely on computer science methods, while others advocate for an integrated approach that combines computational methods with traditional social scientific theory. The goal is to not only predict human behavior but also to interpret and critique it. The field is still defining itself and facing challenges in terms of defining the discipline and addressing biases in data collection and analysis.
Large Language Models as Tools in Social Science Research
Large language models offer powerful tools for social science research, where they can be used to analyze and interpret text data. These models have the potential to revolutionize research methodologies by providing easy-to-use and accessible resources. They can assist in tasks such as classifying political viewpoints, analyzing discourse, and measuring concepts like populism. They have been found to outperform human experts in certain tasks, challenging the notion that certain interpretations are uniquely human. However, it is important to be aware of biases and the need for transparency and control over the models used in research.
The Future of Computational Social Science
The future of computational social science lies in integrating large language models with other computational methods, like agent-based modeling. This combination can help capture the complexity of social interactions and interpretation in a way that traditional models have struggled with. By using large language models as collaborators and iterating on prompt engineering, social scientists can develop more rigorous and nuanced approaches to studying social phenomena. The accessibility of large language models allows researchers to engage with these methods even without extensive programming skills, shifting the focus from technical abilities to theoretical and qualitative skills.
Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational methods and their applications in social sciences. He joins us to discuss findings from his research papers, ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning, and How to use LLMs for Text Analysis.
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