Social media has become a prominent space for seeking and sharing information, but it also enables misinformation to spread. When it comes to disability-related information, such as how to apply for a Medicaid waiver, understanding and detecting the prevalence of false information on social media becomes further complicated due to varying policies that keep changing over time.
News deserts, geographical areas with little or no local news due to a lack of resources or news organizations, pose challenges to civic engagement and democratic life (Abernathy, 2023). Local news not only documents events but also shapes public perception of space and place in particular times by connecting geographic locations through stories. This paper proposes a framework linking news deserts to psychological distance using Construal-Level Theory (CLT) (Trope & Liberman, 2010).
This study examines the dynamics of local community gatherings facilitated by EventBased Social Networking platforms, a growing mode of social interaction in urban settings. While these platforms are increasingly used to organize real-world events, limited research has explored the factors that shape the success of these events across diverse city environments and how local socio-spatial contexts influence participation and engagement.
Purpose: Adolescent school dropout remains a major concern in the United States, accounting for 5.3% of the entire high school students in 2022. This study aims to investigate the relationships between socioeconomic status (SES), the availability of mental health and substance use treatment facilities, and school dropout rates across thirteen states.
This study investigates how the relationships between cities are represented in local news by analyzing co-mentions of cities in 31,004 news articles from Alabama. Using a large language model, we extract geographic references and construct co-mention networks that reflect both spatial proximity and symbolic connections. To interpret these links, we develop a classification framework of relationships between cities, including common impacts and sequential dynamics. Our preliminary analysis reveals that different news categories produce distinct patterns of spatial association.
A wide range of studies has explored historical events and their long-term impacts, with urban redevelopment, particularly in the contexts of urban renewal, gentrification, and redlining, emerging as a rich area of research. However, despite extensive attention to its causes and consequences, quantifying how urban structures have changed over time remains methodologically challenging, as scanned historical maps contain visual noise and annotation.
People with disabilities (PWD) and their family members often find it difficult to find information about available services. One of the approaches to address this information access problem is by understanding the ecology of available information sources. However, identifying the landscape of information sources is challenging due to the variety of sources and their varying visibility. This study proposes a computational approach to processing open-ended survey answers by constructing a hierarchical taxonomy of information sources.
This paper explores the phenomenon of news deserts as information problems to navigate research opportunities and theorize its dynamics. Drawing on the theory of local information landscapes, news deserts are conceptualized as more than merely an absence of news organizations or content; rather, emphasizing the structural and material dimensions of local news ecosystems, such as fragmentation, transience, and inconsistent distribution. We argue that news deserts should be understood as material pre-conditions of people’s access, interpretation, and engagement with information.
Purpose: This study investigated the relationship among information awareness, access, and socioeconomic deprivation in the context of local health information. Drawing on theories of information poverty and the digital divide, this study examined how socio-economic deprivation influences individuals’ ability to become aware of and access local health information.
SAFETI is the first Mason–DOLI Innovation Lab initiative that turns more than 15 years of detailed Virginia workplace-accident records into forward-looking, preventive insights. Using predictive models, the computational approach developed for SAFETI estimates the likelihood of a fatality occurring within a specific time frame and sector, along with its associated probability. This shift from reactive to preventive measures is enabled by advanced spatio-temporal and predictive analytics.