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

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

Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data

This paper shows how to use large-scale pretrained language models to extract character roles from narrative texts without domain-specific training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches

Sensing alterations of the local environment of 3d, 4d, and 4f central ions in polyoxopalladates with soft X-ray magnetic dichroisms

Polyoxopalladates hosting 3d, 4d or 4f central ions form a new class of molecular materials with well-defined, tunable morphology and ions coordination to be considered as model systems for basic research. We investigated the isotropic X-ray absorption near-edge structure (XANES), X-ray magnetic circular dichroism (XMCD), and X-ray magnetic linear dichroism (XMLD) to monitor the influence of symmetry changes on electronic and magnetic properties. After a brief introductory review about recent results on 3d and 4d transition metal ions, we (i) show that a change of the coordination symmetry from cubic eightfold (tetrahedral) to sixfold (octahedral) reduces the magnetic moment of Co2+ significantly and (ii) investigate whether axial distortions can modify the 4f states of trivalent lanthanide ions. While for Gd3+ there is no effect visible within experimental uncertainties, changes were observed for Dy3+ and Ho3+. The results are compared to atomic multiplet calculations including the intra-atomic spin-dipole term . Furthermore, several experimental pitfalls are discussed that may lead to misinterpretation of X-ray absorption data but can be excluded in the present study