LIST OF WORKSHOPS

Over the last decade, interest in using text-as-data approaches in the social sciences has dramatically increased. Large amounts of data, increased computational power, and major advancements in language models such as GPT-4 have allowed, for the first time, to work on text data at scale (Grimmer et al., 2022). This has led to the increasing use of Natural Language Processing (NLP), which includes methods for processing large quantities of text. 

NLP offers a plethora of opportunities for public policy. It has provided new insights on pressing policy issues, such as climate change (Biesbroek et al., 2020; Mallick et al., 2023), and on sectoral policies such as science and technology (Arts et al., 2021). It enables scaling up qualitative research strategies (such as expert coding or content analysis) that otherwise would not be possible (Biesbroek et al., 2020; Crowston et al., 2012), encourages novel research designs (Jones et al., 2022; Lubell et al., 2022) and opens the door to support systems for decision making in policy contexts (Ku & Leroy, 2014; Neumann et al., 2022). This, by applying novel techniques using text-as-data in a variety of documents ranging from legislative or administrative documents to discourses, social media, and news articles. 

Even though NLP has gained prominence in disciplines adjacent to public policy—such as political science and communication science— its application in our discipline remains limited. The majority of applications use NLP as an exploratory technique to study policy domains, particularly through topic modeling (Capano et al., 2020; Kowalski et al., 2020). However, we are convinced that NLP-based methods have considerable potential for research into public policy concepts and theories, and that the methodological toolkit for public policy researchers might change substantially as a result. While applications of NLP in public policy research have remained inductive and exploratory, theory-driven applications of these methods are what holds most promise. Many public policy concepts and theories rely strongly on textual data sources for empirical research, including a variety of different types of local, national, and international policy documents, but also social media posts, media articles or interest group websites.  

NLP has the potential to innovate methods for researching concepts and theories, including agenda setting (Zahariadis, 2016), policy integration (Biesbroek et al., 2020), policy framing (Van Hulst & Yanow, 2014), or policy diffusion (Linder et al., 2020), to name just a few examples. Recent NLP developments promise innovative approaches for these concepts and theories: for instance, they have been used to identify public values in text data (Pelaez et al., 2023), to retrieve context-specific values (Liscio et al., 2021), to extract arguments from opinions (van der Meer et al., 2022), to identify and extract argumentative structures (Lawrence & Reed, 2019), and to automate manual coding of texts (Zhou, 2018). Such developments offer a great potential of innovative ways to approach public policy research. 

Considering the recent NLP advances and their potential in public policy research, we propose this workshop. It has two aims. First, to bring together policy researchers using NLP techniques. Until now, there have been limited venues for public policy researchers using NLP and has limited the establishment of a network of collaborators. We believe this has been detrimental to its adoption in public policy research. Second, the workshop invites to discuss how can NLP approaches be applied to study public policy concepts and theories. In doing so, we invite researchers to present innovative work that go beyond the dominant approach of using NLP for exploratory purposes. The guiding questions of this workshop are: How can Natural Language Processing (NLP) techniques be used to research public policy concepts and theories? Which new research directions does NLP allow policy scholars to pursue?  

This workshop strongly encourages the submission of empirical research using NLP. Our call for papers particularly invites contributions linking such techniques with the advancement of public policy concepts and theories. The intended format of this workshop is paper sessions. A session will consist of several paper presentations, each having two discussants. We will propose a first discussant based on their knowledge of the research topic (e.g., a sectoral policy, a policy theory) and a second discussant based on their familiarity to the NLP technique used in the presentation. Thus, a contributor is expected to give one presentation and comment on two papers. We will finish each session with a brief Q&A round for other participants to engage.  

We also intend to organize, in the final session, an open discussion with the workshop participants on how we can structure the emerging field of NLP for public policy research and discuss ways to go forward. We will propose to the participants a follow-up session in the next IPPA general conference, but also consider other forms of engagement and collaboration.  

Regarding contributors, the chairs of this workshop have already identified potential participants, as the chairs are currently working with researchers using NLP for policy in diverse universities in the Netherlands and abroad. They have expressed interest in participating in this workshop, and we believe our call for papers is of interest to the researchers working with NLP in the IPPA. 

Recent developments in Artificial Intelligence, and particularly in Natural Language Processing (NLP), allow public policy researchers to work with large collections of texts. Such developments offer ample opportunities for taking public policy research into new directions. However, the use of NLP in public policy has been limited, despite its adoption in adjacent disciplines (e.g. political science). We believe this has occurred due to the limited venues for researchers using NLP and due to the fast pace of development of NLP techniques. 
 
For the aforementioned reasons, this workshop has two aims. First, to bring together public policy researchers using NLP for their research, and secondly to discuss innovative approaches in which NLP can be used to study public policy concepts and theories. In doing so, this workshop intends to contribute to accelerate the adoption in public policy research. The guiding questions of this workshop are: How can Natural Language Processing (NLP) techniques be used to research public policy concepts and theories? Which new research directions does NLP allow policy scholars to pursue? 
 
This workshop particularly encourages the submission of empirical research using NLP and other approaches using text-as-data, as well as invites contributions linking such techniques with the advancement of public policy concepts and theories. The workshop format consists of paper presentations, followed by discussions. This workshop intends to organize, in the final session, an open discussion with the workshop participants on how we can structure the emerging field of NLP for public policy research and discuss ways to go forward. 

Over the last decade, the use of Natural Language Processing (NLP) has gained momentum in the social sciences, allowing to work with large quantities of text. NLP offers new possibilities on how to conduct public policy research: for instance, by allowing new qualitative research strategies, collecting data using new methods, or automating activities like coding.  
 
The adoption of NLP in public policy research remains limited, despite having gained prominence in related disciplines, e.g. political science. Moreover, it has been primarily used for exploratory research such as using topic modeling. While applications of NLP in public policy research have remained inductive and exploratory, theory-driven applications of these methods are what holds most promise. For instance, NLP has been used to identify public values (Pelaez et al., 2023), to retrieve context-specific values (Liscio et al., 2021), to extract arguments from opinions (van der Meer et al., 2022), to identify and extract argumentative structures (Lawrence & Reed, 2019), and to automate manual coding of texts (Zhou, 2018). We observe that such methods open exciting opportunities for policy scholars in studying concept and theories, such as agenda setting (Zahariadis, 2016), policy integration (Biesbroek et al., 2020), policy framing (Van Hulst & Yanow, 2014), or policy diffusion (Linder et al., 2020), to name a few examples. 
 
This workshop invites applications of public policy research using NLP techniques. The aims are to bring together policy researchers using NLP and to discuss how can NLP be applied to study public policy concepts and theories. The workshop guiding questions are: How can Natural Language Processing (NLP) techniques be used to research public policy concepts and theories? Which new research directions does NLP allow policy scholars to pursue? It strongly encourages applications of empirical research using NLP and will consist of paper presentations followed by interventions of two discussants. 

Edgar Gironés is a postdoctoral researcher on Natural Language Processing (NLP) for climate change at the Interactive Intelligence Group of Delft University of Technology. Prior to his current appointment, Edgar worked in industry developing NLP techniques and as researcher at the Fraunhofer Institute for Systems and Innovation Research (ISI) in Germany. He holds a PhD in Innovation Governance from Eindhoven University of Technology. His major research interest areas are the application of NLP methods in public policy, particularly in topics of climate change and sustainability, applications of AI in decision and policy making, and the role of ideas in the policy process. 
 
Art Dewulf is Professor of "Sensemaking and decision-making in policy processes" at the Public Administration and Policy Group of Wageningen University and Research. Art Dewulf obtained a PhD in Organizational Psychology on Issue Framing in Multi-Actor Contexts (Leuven, 2006). He studies complex problems of natural resource governance with a focus on interactive processes of sensemaking and decision-making in water and climate governance, across different continents. Three research lines are central to his work: (1) sensemaking and decision-making under uncertainty; (2) sensemaking and decision-making under ambiguity; and (3) data science and AI methods for studying sensemaking and decision-making. 
 
Pradeep K. Murukannaiah is Assistant Professor in the Interactive Intelligence group, Faculty of EEMCS, at the Delft University of Technology (TU Delft), The Netherlands. Pradeep received a PhD in Computer Science (2016) from North Carolina State University, USA. Engineering socially intelligent agents is the overarching theme of Pradeep’s research. He envisions computing systems as sociotechnical systems that support rich interactions between humans and computational agents, enabling a variety of individual and societal applications. Pradeep focuses on developing Natural Language Processing techniques, e.g., argument mining, value extraction, and disagreement detection, to facilitate societal discussions. Pradeep co-directs the Hippo lab, a Delft AI lab on decision support for fair and interpretable public policy, and he is a participating researcher in the Hybrid Intelligence center.