Samenvatting
Purpose: Privacy between users is different from privacy between a user and a third party. OSN, and to a lesser extent researchers, often reduce the former to the latter, which results in misleading users and public debate about privacy. We therefore define two types of privacy, which are distinct but often reduced to each other. Lastly, we also want to investigate which form of privacy is most prominent in privacy settings of OSN.
Methodology: We define two types of privacy that account for the difference between interpersonal and third party disclosure. The fist definition draws on symbolic interactionist accounts of privacy, wherein users are performing dramaturgically for an intended audience (Goffman 1990; Mead & Morris 1992). Third party privacy is based on the data that represent the user in data mining and knowledge discovery processes (Fayyad et al. 1996; Hildebrandt 2006), which ultimately manipulate users into audience commodities (Smythe 1977; Fuchs 2012) We applied this typology to the privacy settings of Facebook, LinkedIn and Twitter. The results are presented as a flowchart.
Findings: Our research indicates that users are granted more options in controlling their interpersonal information flow towards other users than third parties or service providers.
Research implications: This distinction needs to be furthered empirically, by comparing user's privacy expectations in both situations. On more theoretical grounds this typology could also be linked to Habermas' system and life-world.
Originality: We have provided a typology to compare the relative autonomy users receive for settings that drive revenue and settings, which are independent from revenue.
Methodology: We define two types of privacy that account for the difference between interpersonal and third party disclosure. The fist definition draws on symbolic interactionist accounts of privacy, wherein users are performing dramaturgically for an intended audience (Goffman 1990; Mead & Morris 1992). Third party privacy is based on the data that represent the user in data mining and knowledge discovery processes (Fayyad et al. 1996; Hildebrandt 2006), which ultimately manipulate users into audience commodities (Smythe 1977; Fuchs 2012) We applied this typology to the privacy settings of Facebook, LinkedIn and Twitter. The results are presented as a flowchart.
Findings: Our research indicates that users are granted more options in controlling their interpersonal information flow towards other users than third parties or service providers.
Research implications: This distinction needs to be furthered empirically, by comparing user's privacy expectations in both situations. On more theoretical grounds this typology could also be linked to Habermas' system and life-world.
Originality: We have provided a typology to compare the relative autonomy users receive for settings that drive revenue and settings, which are independent from revenue.
Originele taal-2 | English |
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Pagina's (van-tot) | 18-32 |
Aantal pagina's | 15 |
Tijdschrift | Info |
Volume | 16 |
Status | Published - 2014 |