جنبه های اجتماعی حریم خصوصی در شبکه اجتماعی توییتر Collective aspects of privacy in the Twitter social network

1 Introduction Since the leaks of the National Security Agency global surveillance by Edward Snowden [1], privacy in online activity has been one of the rising concerns for Internet users [2]. While these concerns date back nearly two decades [3] and have not led to wide use of privacy-enhancing technologies [4], the topic of privacy rights in online activity is higher than ever on political agenda and media attention. Sharing private information can be motivated by services or information received in exchange, for example in the case of sharing health information with a doctor. Nevertheless, this does not need to be the case in online social networks: A 2016 Pew Research Center survey [5] showed that more than 51% of respondents consider it not acceptable to share private information with an online social network that shows personalized advertisement, in fear of third parties accessing such private data. Social networking sites do not exist in isolation in cyberspace, they exist in the Platform Society [6] and can integrate information from other sites and applications. For several years, more than a billion users of the Facebook mobile applications [7] have given permissions to Facebook to read their phone contact lists.a This motivated the protest of the Europe-vs-Facebook advocacy group about Facebook building shadow profiles [8, 9]: hidden files on individuals with private information that has been inferred through the individual’s friends inside the social network. These shadow profiles can potentially be built for non-users without an account in the social networking site and that did not agree to its privacy policy [10]. The possibility to build shadow profiles would pose an important concern with respect to privacy rights and informational self-determination [11]. Previous research has extensively evaluated privacy risks for users of social media, in general by evaluating the inference of personal attributes of users from their digital traces [12]. For example, Twitter data can be used to predict user locations [13, 14] as well as gender, age, and political orientation of users [15]. Publicly available information in locationbased social networks, such as Foursquare, can also be used to predict the home location of users [16]. Facebook has also been shown to be extremely informative of user personal information, including sexual orientation [17], romantic partnerships [18], and a wealth of other private attributes that can be inferred from user “likes” [19, 20].