Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event fingerprint, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.

To better analyze informal arguments on public forums, we propose the task of argument explication, which makes explicit an argument's structure and implicit reasoning by outputting triples of propositions 〈claim, reason, warrant〉.The three slots, or argument components, are derived from the widely known Toulmin (1958) model of argumentation.While prior research applies Toulmin or related theories to annotate datasets and train supervised models, we develop an effective method to prompt generative large language models (LMs) to output explicitly named argument components proposed by Toulmin.In particular, we prompt a language model with the theory name (e.g., 'According to Toulmin model').We evaluate the outputs' validity through a human study and automatic evaluation based on prior argumentation datasets, and perform robustness checks over alternative LMs, prompts, and argumentation theories.Finally, we conduct a proof-ofconcept case study to extract an interpretable argumentation (hyper)graph from a large corpus of critical public comments on whether to allow the COVID-19 vaccine for children, suggesting future directions for corpus analysis and argument visualization. 1

News coverage profoundly affects how countries and individuals behave in international relations. Yet, we have little empirical evidence of how news coverage varies across countries. To enable studies of global news coverage, we develop an efficient computational methodology that comprises three components: (i) a transformer model to estimate multilingual news similarity; (ii) a global event identification system that clusters news based on a similarity network of news articles; and (iii) measures of news synchrony across countries and news diversity within a country, based on country-specific distributions of news coverage of the global events. Each component achieves state-of-the art performance, scaling seamlessly to massive datasets of millions of news articles.

YouTube has long been a top-choice destination for independent video content creators to share their work. A large part of YouTube's appeal is owed to its practice of sharing advertising revenue with qualifying content creators through the YouTube Partner Program (YPP). In recent years, changes to the monetization policies and the introduction of algorithmic systems for making monetization decisions have been a source of controversy and tension between content creators and the platform. There have been numerous accusations suggesting that the underlying monetization algorithms engage in preferential treatment of larger channels and effectively censor minority voices by demonetizing their content. In this paper, we conduct a measurement of the YouTube monetization algorithms. We begin by measuring the incidence rates of different monetization decisions and the time taken to reach them. Next, we analyze the relationships between video content, channel popularity and these decisions. Finally, we explore the relationship between demonetization and a channel's view growth rate. Taken all together, our work suggests that demonetization after a video is publicly listed is not a common occurrence, the characteristics of the process are associated with channel size and (in unexplainable ways) video topic, and demonetization appears to have a harsh influence on the growth rate of smaller channels. We also highlight the challenges associated with conducting large-scale algorithm audits such as ours and make an argument for more transparency in algorithmic decision-making.

Breastfeeding is not only a public health issue, but also a matter of economic and social justice. This paper presents an iteration of a participatory design process to create spaces for re-imagining products, services, systems, and policies that support breastfeeding in the United States. Our work contributes to a growing literature around making hackathons more inclusive and accessible, designing participatory processes that center marginalized voices, and incorporating systems- and relationship-based approaches to problem solving. By presenting an honest assessment of the successes and shortcomings of the first iteration of a hackathon, we explain how we re-structured the second "Make the Breast Pump Not Suck" hackathon in service of equity and systems design. Key to our re-imagining of conventional innovation structures is a focus on experience design, where joy and play serve as key strategies to help people and institutions build relationships across lines of difference. We conclude with a discussion of design principles applicable not only to designers of events, but to social movement researchers and HCI scholars trying to address oppression through the design of technologies and socio-technical systems.

Networked gatekeepers on social media increasingly influence which people and groups receive media attention. Many unknowingly direct much greater attention to men than to women. Can technologies support these gatekeepers to follow their own values of equality? Theories of value consistency suggest that confronting people with inconsistencies between values and behavior can prompt behavior change. In this paper, we introduce FollowBias, a novel system that offers feedback on the percentage of women that users follow onTwitter. We conduct field deployments of FollowBias with 61 and 78 participants, exploring differences between their values and behavior, their explanations of those differences, and their changes in behavior. In the first, FollowBias users had a 45 percentage point greater chance of increasing the percentage of women followed over one week. In the second, we fail to find an effect. We also offer findings on political and ethical trade-offs in designing systems for behavior change toward equality.

In recent years, the CHI community has begun to discuss how HCI research could improve the experience of motherhood. In this paper, we take up the challenge of designing for this complex life phase and present an analysis of data collected from a design process that included over 1,000 mother-submitted ideas to improve the breast pump, a technology that allows mothers around the world to collect and store their breast milk. In addition to presenting a range of ideas to improve this specific technology, we discuss environmental, legal, social, and emotional dimensions of the postpartum period that suggest opportunities for a range of additional supportive technologies. We close with insights linking our findings to ongoing discussions related to Feminist HCI theory, crowdsourcing, and participatory design.