Olteanu, AlexandraCastillo, CarlosDiaz, FernandoKıcıman, Emre2023-01-172023-01-172019Olteanu A, Castillo C, Diaz F, Kıcıman E. Social data: biases, methodological pitfalls, and ethical boundaries. Front Big Data. 2019 Jul 11;2:13. DOI: 10.3389/fdata.2019.000132624-909Xhttp://hdl.handle.net/10230/55305Social 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.application/pdfeng© 2019 Olteanu, Castillo, Diaz and Kıcıman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.info:eu-repo/semantics/articlehttp://dx.doi.org/10.3389/fdata.2019.00013BiasesEthicsEvaluationSocial mediaUser datainfo:eu-repo/semantics/openAccess