TY - JOUR
T1 - On fairness
T2 - User perspectives on social media data mining
AU - Kennedy, Helen
AU - Elgesem, Dag
AU - Miguel, Cristina
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
KW - Contextual integrity
KW - Digital data
KW - Fairness
KW - Privacy
KW - Social justice
KW - Social media data mining
KW - Surveillance
KW - User perspectives
KW - Well-being
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000401715300003&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1177/1354856515592507
DO - 10.1177/1354856515592507
M3 - Article
SN - 1354-8565
VL - 23
SP - 270
EP - 288
JO - Convergence-the International Journal of Research Into New Media Technologies
JF - Convergence-the International Journal of Research Into New Media Technologies
IS - 3
ER -