The field of cultural NLP has recently experienced rapid growth, driven by a pressing need to ensure that language technologies are effective and safe across a pluralistic user base.This work has largely progressed without a shared conception of culture, instead choosing to rely on a wide array of cultural proxies.However, this leads to a number of recurring limitations: coarse national boundaries fail to capture nuanced differences that lay within them, limited coverage restricts datasets to only a subset of usually highly-represented cultures, and a lack of dynamicity results in static cultural benchmarks that do not change as culture evolves.In this position paper, we argue that these methodological limitations are symptomatic of a theoretical gap.We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap by 1) demonstrating in a case study how it can clarify methodological constraints and affordances, 2) offering theoretically-motivated paths forward to achieving cultural competence, and 3) arguing that localization is a more useful framing for the goals of much current work in cultural NLP.

Information prioritization plays an important role in the way we perceive and understand the world.Homepage layouts, which are daily and manually curated by expert human news editors, serve as a tangible proxy for this prioritization.In this work, we present NewsHomepages, a novel and massive dataset of over 3,000 news website homepages, including local, national, and topic-specific outlets, captured twice daily over a five-year period.We develop a scalable pairwise preference model to capture ranked preferences between news items and confirm that these preferences are stable and learnable: our models infer editorial preference with over 0.7 F1 score (based on human trials).To demonstrate the importance of these learned preferences, we (1) perform a novel analysis showing that outlets across the political spectrum share surprising preference agreements and (2) apply our models to rankorder a collection of local city council policies passed over a ten-year period in San Francisco, assessing their "newsworthiness".Our findings lay the groundwork for leveraging implicit cues to deepen our understanding of human informational preference.

Naitian Zhou, David Jurgens, David Bamman. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Offering condolence is a natural reaction to hearing someone's distress.Individuals frequently express distress in social media, where some communities can provide support.However, not all condolence is equal-trite responses offer little actual support despite their good intentions.Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online.Our study reveals widespread disparity in what types of distress receive supportive condolence rather than just engagement.Building on studies from social psychology, we analyze the language of condolence and develop a new dataset for quantifying the empathy in a condolence using appraisal theory.Finally, we demonstrate that the features of condolence individuals find most helpful online differ substantially in their features from those seen in interpersonal settings.