Schedule

Assigned Room: Gaslamp A&B

• 9:20-9:30 - Opening Remarks

• 9:30-10:30 - Invited Talk #1: Philip Resnik

• 10:30-11:00 - Break 

• 11:00-12:00 - Invited Talk #2: Lucy Li

• 12:00-12:45 - Panel: "What’s the deal with simulating survey responses (or people more generally) with LLMs?"

Georg Ahnert, Andy Halterman, Serina Chang, and Patrick Wu

• 12:45–14:00 - Lunch

• 14:00-15:30 - Poster session (all accepted authors) 

• 15:30-16:00 - Break

• 16:00-17:00 - Invited Talk #3: Stuart Geiger

• 17:00-17:10 - Closing Remarks

Invited talks

Invited Talk #1: Philip Resnik (in-person)

Title: What Does Mental Health Research Need from NLP+CSS, and Vice Versa?

Abstract: Mental health is an incredibly pressing issue in terms of societal, economic, and personal impact.  Although it's natural to conceive of it as a clinical domain, it's also very much a social science domain; moreover, language is one of the most important carriers of signal about individuals' lived experiences and mental state. In this talk I'll discuss the evolving landscape and needs of computational mental health research, what folks interested in NLP+CSS can contribute, and why mental health should be attractive to NLP+CSS researchers as a space to work in. 

Bio: Philip Resnik is a Professor at the University of Maryland with joint appointments in the Department of Linguistics and the Institute for Advanced Computer Studies. In 2020 he was named an ACL Fellow for significant contributions to symbolic-statistical methods for natural language processing, multilinguality, and the interdisciplinary study of language. Philip’s most recent research has focused in three main areas. The first is computational social science, with an emphasis on qualitative analysis and connecting the signal available in people’s language use with underlying mental state. The second is the computational cognitive neuroscience of language, using computational modeling in connection with brain imaging to look at the role of context and predictive processing during online language comprehension. The third involves fundamental questions about how current AI models relate to human cognition and to human society. Outside academia, Philip’s industry experience includes research at Bolt Beranek and Newman and Sun Microsystems Laboratories, as well as an internship at IBM T.J. Watson Research Center, and in entrepreneurial life he has been a technical co-founder of CodeRyte (clinical NLP, acquired by 3M in 2012), an advisor to FiscalNote (machine learning and analytics for government relations, went public in 2022), and he currently serves as an advisor to Trustible (technology provider for responsible AI governance). Philip was an undergrad in CS at Harvard and earned his PhD in Computer and Information Science at the University of Pennsylvania. 

Invited Talk #2: Lucy Li (in-person)

Title: Diaries of Educational Data Research

Abstract: From analyzing school curricula to evaluating AI tutors, I’ve navigated several projects that connect NLP with education. In this talk, I’ll present not only front-facing results of my work, but also go behind the scenes on how they came to be. I’ll discuss ways in which language models are enabling new methods of reworking text for downstream use, and also how they’re introducing risks for students and learning. Throughout, I’ll touch on challenges of interdisciplinarity, and how co-authors from education, social psychology, and edtech have enhanced our joint work.

Bio: Lucy Li is an incoming professor at the University of Wisconsin-Madison’s Computer Sciences department. She is currently a postdoc at the University of Washington advised by Noah Smith and Yulia Tsvetkov. Her research intersects natural language processing with computational social science and digital humanities (e.g. cultural analytics). She has worked with Microsoft Research’s Fairness, Accountability, Transparency, and Ethics (FATE) team and the Allen Institute for AI. She has been recognized by EECS Rising Stars, Rising Stars in Data Science, an American Educational Research Association (AERA) Best Paper Award, and an NSF Graduate Research Fellowship.

Invited Talk #3: Stuart Geiger (in-person)

Title: Recovering the Trace; or, NLP+CSS as Power, Performativity, and Parasight

Abstract: NLP+CSS is often practiced as the more-or-less faithful recovery of objectively-existing social facts from digital traces and other archives. The field's growing concern with construct validity, representativeness, and reproducibility reflects genuine maturity, but is often internal to a self-understanding we must interrogate: not whether NLP+CSS actually measures what it claims, but what must already be true of the social world for there to be anything here for this paradigm to meaningfully measure anything at all. I argue the objectivity of a trace as evidence of some social fact is not found but made: what parts of society get inscribed and interpreted in certain ways and not others is an effect of power, defined in relation to the parts of social life left untraced. NLP+CSS feeds on institutionalized regimes of tracing it did not produce and could not survive without, becoming key infrastructure for a wide range of institutions, while reshaping their authority and reach in turn. Each new finding or model derived from trace data makes it that much more obligatory for platforms and institutions to collect even more trace data to make their subjects more legible to computational analysis at scale. Within this inherent and unavoidable complicity lies a real form of power, and thus responsibility. Given this, what ought the relationship be between NLP+CSS and the wider world that it both relies upon and reshapes?

Bio: Stuart Geiger is an Assistant Professor at UCSD, jointly appointed between the Department of Communication and the Halıcıoğlu Data Science Institute, and affiliate faculty in Computational Social Science, Science Studies, and Computer Science & Engineering. He is a qualitative humanistic social scientist in the traditions of Science and Technology Studies and ethnography, as well as a practicing data scientist, computational social scientist, and software developer. In both modes, his work centers around the ways in which computation, quantification, and automation are modes of power and governance --- especially on digital user-generated content platforms, where he spent the first phase of his career on more community-centered approaches to algorithmic content moderation. On the humanistic side, his current book project is titled The Domains of Data Science, on the relationship between the so-called domain-independent and domain-specific disciplines. On the data science and AI side, his current work involves building societal capacity for critical practice around AI, including through no-code platforms for language model auditing and evaluation (auditomatic.org; sentimentomatic.org), as well as public exhibitions that critically reimagine and restage AI (bartlebyGPT.org; iiiii.stu.lu).

Accepted papers

Borrowed Words, Borrowed Minds: Probing LLM Choice of English-Derived Loanwords in Japanese Joseph James 

Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations Miriam Wanner, Sophia Hager and Anjalie Field

Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts Yi Ren, Lewis Mitchell and Matthew Roughan

Prompt Perturbations Reveal Human-Like Biases in Large Language Model Survey Responses

Jens Rupprecht, Georg Ahnert and Markus Strohmaier 

Launch and Aftermath: Contrasting Social Media Responses to Chatbot Releases. The Cases of Meta’s

Galactica and OpenAI’s ChatGPT Maximilian Weber and Johannes B. Gruber

When Do LLMs Need Human Experts? Evidence for Social Science from Jurisprudential Classification

Caroline Cheng, Edward Stiglitz, David Mimno and Matthew Wilkens

An NLP Framework for Analyzing Corporate Strategic Behavior in the Opioid Industry Documents

Archive Duy Dang Phu and Thìn Đặng Văn 

Beyond Acoustics: Isolating Dialectal and Sociolinguistic Bias in Spanish ASR Johnatan E. Bonilla

Who Speaks for Whom? LLM-Generated Survey Data as a Proxy for Public Opinion Radhakrishnan Venkatakrishnan, Travis Brodbeck and Michael D. Young

Documenting Corporate Harm: A Semantic Action Trajectories Approach to the Opioid Industry Do-

cument Archive Shared Task Ben Miller

Toward Unsupervised Conceptual Metaphor Discovery: A Case Study in Online Immigration Discourse

Alexandria Leto and Maria Leonor Pacheco

Simulating Social Attitudes with LLMs: Accuracy, Demographic Effects, and Refusal Behavior in the

Sensitive Domain of Suicide Prevention Cristina J. Perez, Michael P. Vasquez Jr, Philippe Giabbanelli and Patrick Y. Wu

Gender Disparities in LLM-Based Intimate Partner Violence Detection Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, Christopher M. Danforth and Peter Dodds

Datasets and Methods for Improving the Cultural Capabilities of NLP Systems: A Survey Tania Chakraborty, Eylon Caplan, Zhaoqing Wu, Kevin Cushing, Bruce Qin, Shreya Havaldar

and Dan Goldwasser

Towards More Transparent Online Campaigning: Detecting Political Campaign Content in Election-

related Social Media Posts Abdullah Alabdullah, Conor Gaughan, Thomas Flavel, Shubhanjay Varma, Rachel Gibson, Marta Cantijoch, Alexandru Cernat and Riza Batista-Navarro

Mapping the Landscape of Unregulated eXplicit Contents on Reddit Msvpj Sathvik, Manan Roy Choudhury, Rishita Agarwal, Sathwik Narkedimilli, Thao Ha, Liesel Sharabi and Vivek Gupta 

From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter Yuhang Zhou, Xuan Lu and Wei Ai

Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads Adam Visokay, Ruth Bagley, Chris Hess, Ian Kennedy, Kyle Crowder, Rob Voigt and Denis Peskoff

The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation Yuhang Zhou, Yimin Xiao, Wei Ai and Ge Gao

Non-archival papers:

Belief Is All You Need: Signed Belief Graph Neural Networks for Topic Modeling in Conspiratorial Discourse Soorya Ram Shimgekar, Abhay Goyal, Roy Ka-Wei Lee, Koustuv Saha, Pi Zonooz, Edson C Tandoc Jr, Navin KumarIntergroup 

Bias Affects Expressions of Social Judgment and Empathy in the r/AmITheAsshole Reddit Community Yu Hou, Hal Daumé III, Rachel Rudinger

Can LLMs Annotate Carceral Death Records? A Case Study in Automated Information Extraction from Forensic Autopsy Reports Christina A Chance, Grace Sosa, Kai-Wei Chang, Terence Keel

Measuring Generosity in Collective Bargaining with LLMs, Matthew Bramwell Bone, Prashant Garg, Chenxi Li, Zachary Parolin