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      Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

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          Abstract

          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.

          For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin 1

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          Most cited references392

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          Birds of a Feather: Homophily in Social Networks

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            Experimental evidence of massive-scale emotional contagion through social networks.

            Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.
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              I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience

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                Author and article information

                Contributors
                Journal
                Front Big Data
                Front Big Data
                Front. Big Data
                Frontiers in Big Data
                Frontiers Media S.A.
                2624-909X
                11 July 2019
                2019
                : 2
                : 13
                Affiliations
                [1] 1Microsoft Research , New York, NY, United States
                [2] 2Microsoft Research , Montreal, QC, Canada
                [3] 3Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain
                [4] 4Microsoft Research , Redmond, WA, United States
                Author notes

                Edited by: Juergen Pfeffer, Technical University of Munich, Germany

                Reviewed by: Kenneth Joseph, University at Buffalo, United States; Momin M. Malik, Harvard University, United States

                *Correspondence: Alexandra Olteanu alexandra.olteanu@ 123456microsoft.com

                This article was submitted to Data Mining and Management, a section of the journal Frontiers in Big Data

                Article
                10.3389/fdata.2019.00013
                7931947
                33693336
                313382f5-1ec9-4511-8c4d-d31fe0ed71c5
                Copyright © 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.

                History
                : 26 February 2019
                : 27 May 2019
                Page count
                Figures: 1, Tables: 0, Equations: 0, References: 351, Pages: 33, Words: 32197
                Categories
                Big Data
                Review

                social media,user data,biases,evaluation,ethics
                social media, user data, biases, evaluation, ethics

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