Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013

47 Pages Posted: 20 Dec 2016 Last revised: 23 Jul 2019

Date Written: December 20, 2016


Social data in digital form, which includes user-generated content, expressed or implicit relationships between people, and behavioral traces, are at the core of many 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, product, 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 survey recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We present a framework for identifying a broad variety of menaces in the research and practices around social data use.

Keywords: Social media, user-generated content, behavioral traces, data biases, evaluation, ethical challanges

Suggested Citation

Olteanu, Alexandra and Castillo, Carlos and Diaz, Fernando and Kiciman, Emre, Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (December 20, 2016). Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013, Available at SSRN: or

Carlos Castillo

Universitat Pompeu Fabra

Tànger 122-140
Barcelona, Catalonia 08018

Emre Kiciman

Microsoft Research ( email )

Building 99
Redmond, WA
United States


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