Browsing by Subject "twitter"
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Item @InstitutionalRepository How Do I Preserve Internet Ephemera? #Twitter #Wordpress(2013-03-26) Gaede, Franny; University of Texas at AustinUniversity repositories are tasked with collecting and preserving the intellectual output of their institutions. Colleges and departments are adopting social media to connect with alumni, participate in disciplinary conversations, and encourage community engagement. These ephemeral communications must be captured and preserved to ensure a comprehensive record of the university’s scholarly output. Developing a standard for ingesting and describing these materials poses a novel challenge. This poster will present, as a case study, the specific challenges faced by the University of Texas Digital Repository when preserving the Department of American Studies’ Twitter feed (@AmStudies) and Wordpress blog (AMS::ATX). It will recommend best practices for preservation and access, including content capture, file formats, and metadata standards.Item Local Experts in Social Media(2013-12-04) Bachani, VandanaThe problem of finding topic experts on social networking sites has been a continued topic of research. This thesis addresses the problem of identifying local experts in social media systems like Twitter. Local experts are experts with a topical expertise that is centered around a particular location. This geographically-constrained expertise can be a significant factor for enhanced answering of local information needs (What is the best pub in College Station?), for interacting with local experts (e.g., in the aftermath of a disaster), and for accessing local communities. I developed a local expert finding system ? called OLE (online local experts) ? that leverages the crowd sourced location-topic labels provided by users of the popular Twitter service. Concretely, I mine a collection of 108 million tweets for evidence of local topics of discussion occurring with user-mentions and location pairs; based on this collection, I developed a learning-to-rank approach that incorporates topic-location entropy and a local expert perimeter for varying the expertise focal window. In comparison with alternative expert finding approaches, I find that OLE is quite effective in finding local experts and achieves a 37.72% increase in mean average precision and a 16.8% increase in NDCG scores, across a comprehensive set of queries.