Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding &q...
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Published in: | Frontiers in big data Vol. 2; p. 13 |
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Main Authors: | , , , |
Format: | Journal Article |
Language: | English |
Published: |
Switzerland
Frontiers Media S.A
11-07-2019
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Subjects: | |
Online Access: | Get full text |
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Summary: | Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. "
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 Edited by: Juergen Pfeffer, Technical University of Munich, Germany This article was submitted to Data Mining and Management, a section of the journal Frontiers in Big Data Reviewed by: Kenneth Joseph, University at Buffalo, United States; Momin M. Malik, Harvard University, United States |
ISSN: | 2624-909X 2624-909X |
DOI: | 10.3389/fdata.2019.00013 |