Sensemaking About Contraceptive Methods Across Online Platforms

Selecting a birth control method is a complex healthcare decision. While birth control methods provide important benefits, they can also cause unpredictable side effects and be stigmatized, leading many people to seek additional information online, where they can find reviews, advice, hypotheses, and experiences of other birth control users. However, the relationships between their healthcare concerns, sensemaking activities, and online settings are not well understood. We gather texts about birth control shared on Twitter, Reddit, and WebMD -- platforms with different affordances, moderation, and audiences -- to study where and how birth control is discussed online. Using a combination of topic modeling and hand annotation, we identify and characterize the dominant sensemaking practices across these platforms, and we create lexicons to draw comparisons across birth control methods and side effects. We use these to measure variations from survey reports of side effect experiences and method usage. Our findings characterize how online platforms are used to make sense of difficult healthcare choices and highlight unmet needs of birth control users

#BLM Insurgent Discourse, White Structures of Feeling and the Fate of the 2020 "Racial Awakening"

Working with Twitter data, this paper offers new findings on the #BlackLivesMatter movement and "racial awakening" of summer 2020. Framing methods to address this important moment, this paper contends that cultural studies and critical race studies can be enriched through an engagement with new computational approaches. We analyze how white and racial minority voices talked about race and track their fraught contestation for leadership of racial discourse over the summer of 2020. We uncover a surprising story of white colorblindness even in the midst of a "racial awakening," a story that questions claims that the Trump presidency and the summer of 2020 ushered in a new era of US racial consciousness. And we show how a Black and minority discourse with transformative potential surged and receded. For cultural studies, our data and analysis revise Raymond Williams's influential model of cultural evolution by introducing a new concept: the insurgent, a long-building minority cultural strain that surges to contest the dominant culture in a moment of crisis. For critical race studies, our findings revise prominent theorizations of colorblindness, racial ideology, and hegemony. By revealing the messy and unconscious feelings characterizing colorblindness, our data contest theorizations of colorblindness as an ideology and counter the focus on articulate beliefs in theories of racial hegemony. Ultimately, this paper shows that bringing data methods focused on moments of cultural contestation and mass communication into dialogue with field-specific theory and qualitative analyses can expand our models of how race, discourse, and culture operate

The Crowdsourced “Classics” and the Revealing Limits of Goodreads Data

This presentation draws from forthcoming work on the Goodreads "classics." Goodreads is the largest social networking site for readers on the internet (90 million users) and a subsidiary of Amazon. The “classics” are one of the most active Goodreads categories, with some of the most rated and reviewed books across the entire site. Why are the classics so popular on Goodreads? Which books have readers “shelved” as classics most often? What do the classics mean to contemporary readers? We use computational methods such as topic modeling to investigate these questions and more. We also interrogate the limits of Goodreads data and the influence of Goodreads/Amazon's proprietary algorithms on reviews. We find that reviews sorted by the default algorithm, for example, tend to be longer, more socially conscientious (e.g. include a spoiler alert), and written by a smaller set of Goodreads users. Extrapolating from these findings, we argue that computational methods can provide a way of documenting, understanding, and critiquing algorithmic culture and its effects

The Afterlives of Shakespeare and Company in Online Social Readership

The growth of social reading platforms such as Goodreads and LibraryThing enables us to analyze reading activity at very large scale and in remarkable detail. But twenty-first century systems give us a perspective only on contemporary readers. Meanwhile, the digitization of the lending library records of Shakespeare and Company provides a window into the reading activity of an earlier, smaller community in interwar Paris. In this article, we explore the extent to which we can make comparisons between the Shakespeare and Company and Goodreads communities. By quantifying similarities and differences, we can identify patterns in how works have risen or fallen in popularity across these datasets. We can also measure differences in how works are received by measuring similarities and differences in co-reading patterns. Finally, by examining the complete networks of co-readership, we can observe changes in the overall structures of literary reception

NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs

Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing NLP technologies for clinical use

Riveter: Measuring Power and Social Dynamics Between Entities

Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research

Replication and Computational Literary Studies

The "replication crisis" that has been raging in fields like Psychology (Open Science Collaboration 2015) or Medicine (Ioannidis 2005) for years has recently reached the field of Artificial Intelligence (Barber 2019). One of the key conferences in the field, NeurIPS, has reacted by appointing 'reproducibility chairs' in their organizing committee. In the Digital Humanities, and particularly in Computational Literary Studies (CLS), there is an increasing awareness of the crucial role played by replication in evidence-based research. Relevant disciplinary developments include the increased importance of evaluation in text analysis and the increased interest in making research transparent through publicly accessible data and code (open source, open data). Specific impulses include Geoffrey Rockwell and Stéfan Sinclair's re-enactments of pre-digital studies (Sinclair and Rockwell 2015) or the recent replication study by Nan Z. Da (Da 2019). The paper has been met by an avalanche of responses that pushed back several of its key claims, including its rather sweeping condemnation of the replicated papers. However, an important point got buried in the process: that replication is indeed a valuable goal and practice. As stated in the Open Science Collaboration paper: "Replication can increase certainty when findings are reproduced and promote innovation when they are not" (Open Science Collaboration 2015, 943). As a consequence, the panel aims to raise a number of issues regarding the place, types, challenges and affordances, both on a practical and on a policy or community level, of replication in CLS. Several impulse papers will address key aspects of the issue: recent experience with attempts at replication of specific papers; policies dealing with replication in fields with more experience in the issue; conceptual and terminological clarification with regard to replication studies; and proposals for a way forward with replication as a community task or a policy issue

Intramolecular crossover from unconventional diamagnetism to paramagnetism of palladium ions probed by soft X-ray magnetic circular dichroism

The case of palladium(II) ions in molecular polyoxopalladates highlights the importance of accounting not only for nearest neighbour atoms or ions in order to understand, model or predict magnetic characteristics. Here, using site-specific soft X-ray magnetic circular dichroism (XMCD), the effects of different bond lengths, delocalization of 4d electrons, and 4d spin-orbit coupling on the electronic and magnetic properties are investigated and three different states identified: Conventional diamagnetism in a square-planar O4 coordination environment, paramagnetism caused by four additional out-of-plane oxygen anions, and an unusual diamagnetic state in the diamagnetic/paramagnetic crossover region modified by significant mixing of states and facilitated by the substantial 4d spin-orbit coupling. The two diamagnetic states can be distinguished by characteristic XMCD fine structures, thereby overcoming the common limitation of XMCD to ferro-/ferrimagnetic and paramagnetic materials in external magnetic fields. The qualitative interpretation of the results is corroborated by simulations based on charge transfer multiplet calculations and density functional theory results

Modeling Personal Experiences Shared in Online Communities

149 pagesWritten communications about personal experiences, such as giving birth or reading a book, can be both rhetorically powerful and statistically difficult to model. My research explores unsupervised natural language processing (NLP) models to represent complex personal experiences and self-disclosures communicated in online communities, while also re-examining these models for biases and instabilities. I seek to reliably represent individual experiences within their social contexts and model interpretive dimensions that illuminate both patterns and outliers, while addressing social and humanistic questions. Through this work, I develop a data science practice that emphasizes cross-disciplinary collaborations and care for datasets and their authors. In this dissertation, I share case studies that highlight both the opportunities and the risks in reusing NLP models for context-specific research questions

Extracting Topically Related Synonyms from Twitter using Syntactic and Paraphrase Data

Thesis (Master's)--University of Washington, 2014The goal of synonym extraction is to automatically gather synsets (groups of synonyms) from a corpus. This task is related to the tasks of normalization and paraphrase detection. We present a series of approaches for synonym extraction on Twitter, which contains unique synonyms (e.g. slang, acronyms, and colloquialisms) for which no traditional resources exist. Because Twitter contains so much variation, we focus our extraction on certain topics. We show that this focus on topics yields significantly higher coverage on a corpus of paraphrases than previous work which was topic-insensitive. We demonstrate improvement on the task of paraphrase detection when we substitute our extracted synonyms into the paraphrase training set. The synonyms are learned by using chunks from a shallow parse to create candidate synonyms and their context windows, and the synonyms are incorporated into a paraphrase detection system that uses machine translation metrics as features for a classifier. When we train and test on the paraphrase training set and use synonyms extracted from the same paraphrase training set, we find a 2.29\% improvement in F1 and demonstrate better coverage than previous systems. This shows the potential of synonyms that are representative of a specific topic. We also find an improvement in F1 score of 0.81 points when we train on the paraphrase training set and test on the test set and use synonyms extracted with an unsupervised method on a corpus whose topics match those of the paraphrase test set. We also demonstrate an approach that uses distant supervision, creating a silver standard training and test set, which we use both to evaluate our synonyms and to demonstrate a supervised approach to synonym extraction