Folk models of online behavioral advertising

Yaxing Yao, Davide Lo Re, Yang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationCSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages1957-1969
Number of pages13
ISBN (Electronic)9781450343350
DOIs
StatePublished - Feb 25 2017
Event2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 - Portland, United States
Duration: Feb 25 2017Mar 1 2017

Other

Other2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
CountryUnited States
CityPortland
Period2/25/173/1/17

Keywords

  • Mental model
  • Online Behavioral Advertising (OBA)
  • Privacy-enhancing technologies (PETs)
  • Web tracking

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Yao, Y., Re, D. L., & Wang, Y. (2017). Folk models of online behavioral advertising. In CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1957-1969). Association for Computing Machinery. https://doi.org/10.1145/2998181.2998316