COVID-19 has killed hundreds of thousands, constituting a major global crisis. Laypeople bear a large burden of responsibility for perceiving risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information.
Independent business owners often prefer informal information sources to formal ones such as library collections. Part of these preferences is rational because contextual and application-oriented information is usually available from informal sources, which are theoretically the best matches for this kind of information. This suggests that, in addition to outreach strategies, efforts to integrate informal sources of business-relevant information can improve public libraries' ability to support independent business owners' information needs.
KNEXT is a three-year collaborative project between Kent State University (KSU-SLIS) and the University of Maryland (UMD-CIS), which partners with local public libraries, small business development centers, economic development organizations, and community advocacy groups to bring advanced data analytics and business intelligence (DA&BI) services to public libraries in order to support small businesses, entrepreneurs, and community advocates within two recovering communities in Ohio and Maryland.
This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing.
This project aims to build a map-based platform that presents historical documents of the nation-wide, urban renewal project in 1960's and 70's and to provide easy-to-use interfaces that can be used by former residents, archivists, researchers, and citizens. Ultimately, this platform aims to reconstruct a virtual neighborhood where people can share their memories by creating social networks of former residents.
Social media has provided a huge amount of user-generated data in capturing urban dynamics. Among them, place-level human behavior has been largely detected through people’s check-in records at certain places. Conventionally, places are characterized by a set of pre-defined features, often specified by the owner of the places. In this paper, we argue that capturing socially-meaningful features and dynamics of an urban place may also be done by analyzing human activity traces.
In this paper, we investigate the role of sociocultural contexts and technological characteristics in user behaviors on social networking sites (SNSs). This study focuses on Korean mothers’ social roles and their use of KakaoStory—one of the most popular SNSs in Korea. Through interviews with fifteen Korean mother users, this research studies changing social roles of Korean mothers with childbirth, and its influence on KakaoStory use. Also, we investigate how KakaoStory’s unique characteristics affect mothers’ usage.
This paper presents the findings from a project about how international students seek and acquire information during their settlement in an unknown geo-spatial environment. Through semi-structured interviews, questionnaires, and cognitive mapping with twenty international students, this study examines their information needs, information sources, and settlement experiences in the host country.