Abstract: Chapter 2 Total Twitter Error Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective Yuli Patrick Hsieh, Yuli Patrick Hsieh Survey Research Division, RTI International, Chicago, IL, USASearch for more papers by this authorJoe Murphy, Joe Murphy Survey Research Division, RTI International, Chicago, IL, USASearch for more papers by this author Yuli Patrick Hsieh, Yuli Patrick Hsieh Survey Research Division, RTI International, Chicago, IL, USASearch for more papers by this authorJoe Murphy, Joe Murphy Survey Research Division, RTI International, Chicago, IL, USASearch for more papers by this author Book Editor(s):Paul P. Biemer, Paul P. Biemer RTI International and University of North CarolinaSearch for more papers by this authorEdith de Leeuw, Edith de Leeuw Utrecht UniversitySearch for more papers by this authorStephanie Eckman, Stephanie Eckman RTI InternationalSearch for more papers by this authorBrad Edwards, Brad Edwards WestatSearch for more papers by this authorFrauke Kreuter, Frauke Kreuter Joint Program in Survey Methodology, University of Mannheim, Institute for Employment Research (Germany)Search for more papers by this authorLars E. Lyberg, Lars E. Lyberg InizioSearch for more papers by this authorN. Clyde Tucker, N. Clyde Tucker American Institutes for ResearchSearch for more papers by this authorBrady T. West, Brady T. West University of Michigan and Joint Program in Survey MethodologySearch for more papers by this author First published: 27 January 2017 https://doi.org/10.1002/9781119041702.ch2Citations: 27 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary The total survey error (TSE) framework presents a structural approach to the procedural and statistical errors of survey estimates with the goal of ensuring the data quality for subsequent analysis and inferences. Through the lens of TSE, this chapter conceptualizes the errors that can result from the common practice of social media data extraction and analysis, identifies the trade-offs between data and errors across queries. In completing this exercise, it gives a general error framework for Twitter opinion research comprising three broad and interrelated but exhaustive and mutually exclusive error sources: coverage error, query error, and interpretation error. The chapter provides an overview of the literature describing how the architecture and the user-generated content of Twitter may reflect public opinion. It illustrates the major types of Twitter errors by examining public opinion on a few case studies using the identical research design and data extraction procedures. Citing Literature Total Survey Error in Practice RelatedInformation
Publication Year: 2017
Publication Date: 2017-01-27
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 42
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