Scientists & Women Scientists: Exploring Gender Biases in Institutional Category Systems

For many categories of people, men are perceived as the more default or typical members whereas women are perceived as more atypical. This bias can lead to an asymmetry in the existence and frequency of categories marked by gendered language. Here we explore the extent to which this asymmetry exists in two institutional category systems: the Library of Congress Subject Headings (LCSH) and English Wikipedia. We find that the LCSH exhibits more bias towards women than Wikipedia, and that in the LCSH this bias has not changed in the last 30 years, whereas Wikipedia shows a noticeable increase in gender balanced categories during the early 2010s. These findings suggest that more can be done to reduce gender bias in the LCSH and demonstrate how principles of typicality and categorization play out in real-world settings

Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing

Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative. In this work, we revisit a widely used conceptualization of framing from the communication sciences which explicitly captures elements of narratives, including conflict and its resolution, and integrate it with the narrative framing of key entities in the story as heroes, victims or villains. We adapt an effective annotation paradigm that breaks a complex annotation task into a series of simpler binary questions, and present an annotated data set of English news articles, and a case study on the framing of climate change in articles from news outlets across the political spectrum. Finally, we explore automatic multi-label prediction of our frames with supervised and semi-supervised approaches, and present a novel retrieval-based method which is both effective and transparent in its predictions. We conclude with a discussion of opportunities and challenges for future work on document-level models of narrative framing.Comment: To appear in ACL 2023 (main conference

Modelling compounding across languages with analogy and composition

Compounding is a common word formation process in many languages around the world. Previous semantic analyses of compounding suggest that analogy and composition are crucial cognitive processes that underlie the formation of new compounds, but these processes are typically considered separately. Here, we formulate a computational model of compounding that integrates both analogy and composition. Compared to simpler baselines, we show that the model combining both processes achieves the best performance in predicting the constituents of attested compounds in English, Chinese, and German. Our work extends previous semantic-based accounts of compounding via a computational approach that can be evaluated using large-scale crosslinguistic data

Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing

The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community recently. While NLP can help to scale up analyses or contribute automatic procedures to investigate the impact of biased news in society, we argue that methodologies that are currently dominant fall short of addressing the complex questions and effects addressed in theoretical media studies. In this survey paper, we review social science approaches and draw a comparison with typical task formulations, methods, and evaluation metrics used in the analysis of media bias in NLP. We discuss open questions and suggest possible directions to close identified gaps between theory and predictive models, and their evaluation. These include model transparency, considering document-external information, and cross-document reasoning rather than single-label assignment.Comment: Accepted to the sixth Workshop on Natural Language Processing and Computational Social (NLP+CSS