Profiling Facebook Users’ Privacy Behaviors Pamela Wisniewski Bart P. Knijnenburg Heather Richter Lipford College of Information Sciences and Department of Informatics Department of Software and Technology University of California, Irvine Information Systems The Pennsylvania State University [email protected] UNC Charlotte [email protected] [email protected] ABSTRACT different interface features available for regulating interpersonal Social Network Sites (SNSs) such as Facebook offer a plethora of privacy [23]. By doing this, we were able to build a theoretical privacy controls, but users rarely exploit all of these controls, nor framework to better understand the various types of interpersonal do they do so in a similar manner. In this paper, we analyze privacy boundaries that SNS users manage [21, 23]. In many distinct profiles of users’ privacy management strategies on cases, we found that the ability to manage various types of Facebook (including but also going beyond information disclosure interpersonal boundaries was directly dependent on the interface behavior). We cluster the self-reported privacy behaviors of 308 features available within the SNS for doing so. Therefore, for the Facebook users based on the privacy settings and features purposes of this paper, we define privacy behaviors as the privacy available in Facebook’s user interface. We extrapolate six distinct features and/or settings that Facebook users leverage in order to privacy profiles, which include: 1) Privacy Maximizers, 2) manage interpersonal privacy boundaries. On Facebook, Selective Sharers, 3) Privacy Balancers, 4) Self-Censors, 5) Time managing one’s personal user profile information, the content Savers/Consumers, and 6) Privacy Minimalists. Creating such displayed or posted onto one’s Timeline or Wall, the content that profiles will enable deeper exploration of privacy concerns and filters into one’s News Feed from one’s friends, or even whom behaviors, as well as expose opportunities for personalization of one chooses to friend or unfriend are all examples of interpersonal privacy settings, recommendations, and training. boundary decisions that SNS users can combine to form a strategy for regulating their interpersonal privacy boundaries. 1. INTRODUCTION A variety of research has examined individuals’ use of various Privacy is a major concern of Social Network Site (SNS) users privacy controls, and their relationships with privacy concerns, [13], even though most SNSs provide users with a variety of demographics, or other behaviors and outcomes. For example, mechanisms to control how they interact and share information Stutzman et al. [17] examined the factors which contributed to with one another. Users’ efficacy in privacy management is Facebook users’ decisions on whether or not to set their Facebook hampered by their bounded rationality [1] and their limited profiles to “Friends Only.” Ellison et al. [5] found a positive motivation to control their privacy [4, 6]. Thus, understanding and relationship between Facebook users’ use of advanced privacy exploiting all the mechanisms necessary to manage every aspect settings (such as changing privacy settings from the default and of a one’s privacy on an SNS such as Facebook is nearly limiting content sharing to specific groups within one’s network) impossible. In this paper, we demonstrate that Facebook users and perceived social capital, the benefits derived from being an instead use a subset of the available mechanisms to manage their active member of a social network. Other researchers have also privacy. We find that not every user leverages the same subset of explored the use of selective sharing through friend lists or circles privacy mechanisms and uncover distinct profiles of behavior that [7, 20]. give insight into different users’ privacy management strategies. The majority of privacy research has focused on privacy settings 2. BACKGROUND as they relate specifically to information disclosure behaviors [10- Our work frames privacy in a broad sense as, “an interpersonal 12, 16, 19]. Yet few studies have examined overarching privacy boundary process by which a person or group regulates interaction management strategies of SNS users: How do users employ with others,” by altering the degree of openness of the self to various subsets of the available mechanisms to manage their others [2]. Managing information disclosures is just one strategy privacy and how do these strategies vary across users? In this SNS users employ to manage their interpersonal privacy with paper, we investigate the dimensionality of various privacy others. For example, some SNS users leverage friend lists in behaviors on Facebook and classify users into different privacy Facebook or circles in Google+ in order to disclose more personal profiles based on these dimensions. This work moves beyond information but to smaller audiences [7, 20, 23]. Others adopt Knijnenburg et al. [10] and other SNS privacy research by coping strategies, such as managing multiple Facebook profiles or analyzing not just information disclosure behaviors, but a wide using pseudonyms to prevent different social circles from over- range of available privacy management strategies based upon our lapping or engaging with unwanted others [22]. Previously, we previous feature analysis. In the next section, we describe our data conducted a feature-oriented domain analysis across five popular collection procedures and method of analysis for examining the SNS websites, including Facebook to conceptually group the underlying dimensionality of different privacy behaviors and classifying Facebook users based on varying levels of these dimensions. Then, we present the different dimensions of privacy behavior and describe six unique privacy profiles that emerge Copyright is held by the author/owner. Permission to make digital or from our analysis. Finally, we discuss the potential use of these hard copies of all or part of this work for personal or classroom use is granted without fee. privacy profiles in further understanding and supporting SNS users’ privacy needs. Symposium on Usable Privacy and Security (SOUPS) 2014, July 9-11, 2014, Menlo Park, CA. 1 3. PROCEDURE following to modify posts on your News Feed?” The privacy behaviors for altering one’s News Feed (NWF, see Figure 1) 3.1 Data Collection included the frequency (1 = Never, 7 = Always) in which users: 1) Data were collected through a web-based survey using Survey Hid a story, 2) Reported Story or Spam, 3) Changed friend Share. Participants had to be over 18 and have an active Facebook subscription settings, 4) Unsubscribed from a friend, or 5) account. We asked them to simultaneously login to their Facebook Unsubscribed from status updates from a friend. accounts in order to report various privacy behaviors and settings. If a feature supported multiple behaviors, we asked a separate Participant recruitment was done through snowball sampling [3] question for each behavior. Privacy behaviors that were in-situ using two different methods: First, the primary researcher seeded were measured on a 7-point scale ranging from “Never” to the snowball through her personal SNSs, (such as Facebook “Always” on how frequently they used a particular privacy feature Twitter, and LinkedIn), via email, and posting to Craigslist’s (similar to the example for Figure 1) or by a count of behavior volunteer’s message board in her local city. Second, a random frequency. For instance, we asked users to report how many users sample of 5,000 university email addresses were selected and they had blocked (BLU), ranging from 1 = None to 5 = More than emailed an invitation to participate in the survey. Participation ten. Privacy behaviors that were tied to a specific privacy setting was incentivized through a drawing with a chance to win one of were measured based on the actual options provided by the two $200 Amazon gift certificates. Each participant who opted in Facebook interface. For example, participants were asked to received one drawing entry. As an extra incentive to share the report their Facebook profile settings for their “Basic Info” (BAS survey, participants received one additional entry for each in Table 1). Possible responses included, “I did not provide this successful referral, up to a maximum of 25 entries. information to Facebook,” “Public,” “Friends,” “Only Me,” 3.2 Method of Analysis “Custom,” and “Any customized friend list.” These responses were coded from 1 = least private to N = most private, given the 3.2.1 Operationalization of Constructs number of options provided by Facebook. In our domain-oriented feature analysis [23], our goal was to methodically identify the full set of Facebook privacy settings and 3.2.2 Data Analysis Approach features that were available within the interface for negotiating We adapted Knijnenburg et al.’s approach to analyzing the interpersonal boundary regulation. We leveraged these findings in privacy behavior items in our dataset [10]. First, using a our current study to provide participants with written directions Confirmatory Factor Analysis (CFA) with a weighted least and a screenshot on how to access these various settings and squares estimator [8, 15], we verified the multidimensionality of features. Next, we asked participants about specific actions they our privacy behavior items1. We adjusted the resulting factors (i.e. had previously taken using each privacy setting or feature. All removing items, splitting and combining factors) until we questions asked regarding privacy behaviors are displayed in achieved a satisfactory fit of the model to the data. Next, we Table 1. Question order was optimized to reduce the number of performed a series of Mixture Factor Analyses (MFAs) with a clicks participants needed to take to access the various settings or robust maximum likelihood estimator [14-15]. MFA first features once they logged into their Facebook accounts. establishes a CFA model and then sorts participants into a Figure 1 shows an example of the privacy options for managing specified number of classes, where each class is allowed to have a the content that filters into one’s Facebook News Feed. What different specific value on each of the factors.