TY - GEN
T1 - Folk models of online behavioral advertising
AU - Yao, Yaxing
AU - Re, Davide Lo
AU - Wang, Yang
N1 - Funding Information:
We thank our interviewees for sharing their insights. We also thank Alexander Krapf, Satoko Mii, Eduardo Nunez-Amador, Huichuan Xia for their assistance as well as Jason Dedrick and anonymous reviewers for their thoughtful comments on earlier versions of this paper. This work was supported in part by NSF Grant CNS-1464347.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/2/25
Y1 - 2017/2/25
N2 - Online Behavioral Advertising (OBA) is pervasive on the Internet. While there is a line of empirical research that studies Internet users' attitudes and privacy preferences of OBA, little is known about their actual understandings of how OBA works. This is an important question to answer because people often draw on their understanding to make decisions. Through a qualitative study conducted in an iterative manner, we identify four "folk models" held by our participants about how OBA works and show how these models are either incomplete or inaccurate in representing common OBA practices. We discuss how privacy tools can be designed to consider these folk models. In addition, most of our participants felt that the information being tracked is more important than who the web trackers are. This suggests the potential for an information-based blocking scheme rather than a tracker-based blocking scheme used by most existing adblocking tools.
AB - Online Behavioral Advertising (OBA) is pervasive on the Internet. While there is a line of empirical research that studies Internet users' attitudes and privacy preferences of OBA, little is known about their actual understandings of how OBA works. This is an important question to answer because people often draw on their understanding to make decisions. Through a qualitative study conducted in an iterative manner, we identify four "folk models" held by our participants about how OBA works and show how these models are either incomplete or inaccurate in representing common OBA practices. We discuss how privacy tools can be designed to consider these folk models. In addition, most of our participants felt that the information being tracked is more important than who the web trackers are. This suggests the potential for an information-based blocking scheme rather than a tracker-based blocking scheme used by most existing adblocking tools.
KW - Mental model
KW - Online Behavioral Advertising (OBA)
KW - Privacy-enhancing technologies (PETs)
KW - Web tracking
UR - http://www.scopus.com/inward/record.url?scp=85014748737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014748737&partnerID=8YFLogxK
U2 - 10.1145/2998181.2998316
DO - 10.1145/2998181.2998316
M3 - Conference contribution
AN - SCOPUS:85014748737
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 1957
EP - 1969
BT - CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery
T2 - 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
Y2 - 25 February 2017 through 1 March 2017
ER -