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COVID-19 has been a sustained and global crisis with a strong continual impact on daily life. Staying accurately informed about COVID-19 has been key to personal and communal safety, especially for essential workers— individuals whose jobs have required them to go into work throughout the pandemic—as their employment has exposed them to higher risks of contracting the virus. Through 14 semi-structured interviews, we explore how essential workers across industries navigated the COVID-19 information landscape to get up-to-date information in the early months of the pandemic. We find that essential workers living through a sustained crisis have a broad set of information needs. We summarize these needs in a framework that centers 1) fulfilling job requirements, 2) assessing personal risk, and 3) keeping up with crisis news coverage. Our findings also show that the sustained nature of COVID-19 crisis coverage led essential workers to experience breaking points and develop coping strategies. Additionally, we show how workplace communications may act as a mediating force in this process: lack of adequate information in the workplace caused workers to struggle with navigating a contested information landscape, while consistent updates and information exchanges at work could ease the stress of information overload. Our findings extend the crisis informatics field by providing contextual knowledge about the information needs of essential workers during a sustained crisis. 

During the COVID-19 pandemic, local news organizations have played an important role in keeping communities informed about the spread and impact of the virus. We explore how political, social media, and economic factors impacted the way local media reported on COVID-19 developments at a national scale between January 2020 and July 2021. We construct and make available a dataset of over 10,000 local news organizations and their social media handles across the U.S. We use social media data to estimate the population reach of outlets (their “localness”), and capture underlying content relationships between them. Building on this data, we analyze how local and national media covered four key COVID-19 news topics: Statistics and Case Counts, Vaccines and Testing, Public Health Guidelines, and Economic Effects. Our results show that news outlets with higher population reach reported proportionally more on COVID-19 than more local outlets. Separating the analysis by topic, we expose more nuanced trends, for example that outlets with a smaller population reach covered the Statistics and Case Counts topic proportionally more, and the Economic Effects topic proportionally less. Our analysis further shows that people engaged proportionally more and used stronger reactions when COVID-19 news were posted by outlets with a smaller population reach. Finally, we demonstrate that COVID-19 posts in Republican-leaning counties generally received more comments and fewer likes than in Democratic counties, perhaps indicating controversy. 

Extending the benefits of online reading to people with reading disabilities such as dyslexia requires broader research on reading behavior in addition to existing small-scale eye-tracking studies. We conduct the first large-scale mixed-methods study of the unique reading challenges of people with dyslexia. We combine in-person interviews (N=6), online surveys (N=566) and a novel browser-based tool able to measure detailed reading behavior remotely on a controlled set of five pages (N=477) or as a browser extension (N=89) collecting long-term reading behavior data on self-selected pages. We find a variety of text and page layout factors that pose challenges to readers with and without dyslexia, and identify in-browser reading behaviors associated with dyslexia. Findings point toward improvements to technologies for identifying struggling readers, and to ways to improve the layout and appearance of online articles to improve reading ease for people with and without dyslexia. 

Rich engagement data can shed light on how people interact with online content and how such interactions may be determined by the content of the page. In this work, we investigate a specific type of interaction, backtracking, which refers to the action of scrolling back in a browser while reading an online news article. We leverage a dataset of close to 700K instances of more than 15K readers interacting with online news articles, in order to characterize and predict backtracking behavior. We first define different types of backtracking actions. We then show that “full” backtracks, where the readers eventually return to the spot at which they left the text, can be predicted by using features that were previously shown to relate to text readability. This finding highlights the relationship between backtracking and readability and suggests that backtracking could help assess readability of content at scale.