On fairness

Abstract

What do social media users think about social media data mining? To date, this question has been researched through quantitative studies that produce diverse findings and qualitative studies adopting either a privacy or a surveillance perspective. In this article, we argue that qualitative research which moves beyond these dominant paradigms can contribute to answering this question, and we demonstrate this by reporting on focus group research in three European countries (the United Kingdom, Norway and Spain). Our method created a space in which to make sense of the diverse findings of quantitative studies, which relate to individual differences (such as extent of social media use or awareness of social media data mining) and differences in social media data mining practices themselves (such as the type of data gathered, the purpose for which data are mined and whether transparent information about data mining is available). Moving beyond privacy and surveillance made it possible to identify a concern for fairness as a common trope among users, which informed their varying viewpoints on distinct data mining practices. We argue that this concern for fairness can be understood as contextual integrity in practice (Nissenbaum, 2009) and as part of broader concerns about well-being and social justice.

Trust, Disconnection, Minimizing risk and Apathy

“…Similarly to Kennedy et al (2017) we saw differences between young and old, and between educated and less educated, although our data cannot be generalized. It remains an important question to examine whether people of different ages and backgrounds perceive risk and dependence in different ways, or if similar coping tactics can be found in similar groups.…”

Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media

“…Reversely, algorithms that misrepresented young people's tastes or were too simplistic appropriations of their interests invoked irritation. Knowing you are being surveilled raises expectations: there should be a fair trade-off between giving up data privacy and ease of use (Kennedy et al, 2017). Simultaneously, however, algorithms that functioned too well created the uncomfortable feeling of being watched or even exploited, especially when users could not logically deduce its assumptions from the data they had consciously supplied.…”

Awareness of Indirect Information Disclosure on Social Network Sites

“…Moreover, individuals react and behave differently based on the sensitivity of their information; and therefore, privacy behavior is context-dependent (Acquisti et al, 2015). In contrast to previous research (e.g., Kennedy et al, 2015;Marwick & Hargittai, 2018) that studied privacy attitudes from a holistic view (i.e., not considering the granularity of personal information), this research sheds more light on the attitudes towards indirect disclosure through two specific information types, namely, photos and posts. The results suggest that inferred information from SNS user data (photos and posts) disrupts boundaries within the genres of disclosure.…”

“…Surveys rely on self-reported behavior, which often differs from actual behavior (Hughes-Roberts, 2013;Kokolakis, 2017). The dominant research on secondary use is based on surveys (Dinev & Hart, 2006;Ham, 2017;Kim & Huh, 2017;Son & Kim, 2008), while other papers have been largely based on researcher-centric preconceptions of privacy concerns, often slightly disconnected from users' own interpretations of privacy, for example, fictional case scenarios (Culnan, 1993;Krasnova, Hildebrand, & Guenther, 2009) and scenarios concerning real-world social-media data-mining activities (Kennedy, Elgesem, & Miguel, 2015). One factor that leads to the privacy paradox is the users' lack of awareness of possibilities to protect their privacy due to incomplete information about how institutions use or misuse their information (Barth & de Jong, 2017;Raynes-Goldie, 2010).…”