Browsing by Subject "covid-19"
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Item CSRC Oral History: Sogorea Te’ Land Trust Interviews(2020-08-05) Brown, Nazshonnii; Morales, Harold; Wheeler, Kayla; Department of Philosophy and Religious Studies; Center for the Study of Religion and the City (CSRC)The Sogorea Te’ Land Trust is an urban Indigenous women-led land trust based in the San Francisco Bay Area that returns Indigenous land to Indigenous people. It was founded in 2012 with the goals of returning traditionally Chochenyo and Karkin lands in the San Francisco Bay Area to Indigenous stewardship and cultivating more active, reciprocal relationships with the land. Through the practices of rematriation, cultural revitalization, and land restoration, Sogorea Te’ calls on native and non-native peoples to heal and transform the legacies of colonization, genocide, and patriarchy and to do the work our ancestors and future generations are calling us to do. The CSRC grant will be used to expand food production and distribution for members of urban Indigenous communities who have been affected by COVID-19.Item Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs(2020-05-06) Ordun, Catherine; Purushotham, Sanjay; Raff, EdwardThis paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.Item The Impacts of Covid-19 on Small Businesses(2024-04-25) Kathryn Phillips; Sanders, Jolene; Gurzick, David; Yankuo, Qiao; Hood College Department of Sociology and Social Work; Hood College Departmental HonorsItem Understandings of Science Advocacy can Strengthen it(Union of Concerned Scientists, 2020-08-19) Tormos-Aponte, Fernando