Abstract
What is the nature of personal privacy in an increasingly digital world? To what extent should we foster greater information exchange among the public at large, versus protect the ability to limit disclosure to the people of one’s choosing? And to what extent do people say they care about either? Previous research on online privacy has predominantly been concerned with questions such as these. Noticeably absent, however, has been research examining actual online privacy behavior and its causes. In other words, regardless of whether people say they care about online privacy – and regardless of whether they should care about online privacy – given the option to disclose more information or less, what factors are predictive of the actual privacy decision that people make?
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Notes
- 1.
It is also possible that individuals self-segregate based on structural position – people with many ties befriending other people with many ties, and people with few ties befriending other people with few ties (Newman 2002). Such “degree-based” assortative mixing is not considered here.
- 2.
In stochastic actor-based modeling, one typically also controls for potential curvilinearity in this tendency by including a quadratic term. This is unnecessary here because the behavioral variable is dichotomous.
- 3.
Because it is not possible to distinguish between a student who is not on Facebook and a student who is on Facebook but has hidden herself from searches, I first restricted attention to only those students who could be located on Facebook for all 4 years. Of the 1,421 students who remained in the study cohort for all 4 years, 1,272 (89.5%) met this criterion. The remaining 396 students who were dropped from my analyses were active on Facebook for all 4 years, but did not have available network data for at least one year. Comparing these 396 students with the final population of 876, dropped students were significantly more likely to have a private profile in every wave – creating some risk of selection bias – and significantly more likely to be Asian. Otherwise, however, the two samples were statistically indistinguishable with respect to gender, race, and socioeconomic status.
- 4.
An alternative approach would have been to simply maximize the available data for each transition period separately (see below). However, this would have the undesirable consequence that results could no longer be compared over time, because each model would be estimated over a slightly different subset of students.
- 5.
It is important to note that this dataset was not compiled with the intention of studying privacy behavior, and hence some distortion in the central behavioral dependent variable was introduced insofar as research assistants were recruited from the college of study. Consequently, an unknown minority of students in the study population may have falsely appeared to have “public” profiles if they happened to be Facebook friends with the specific research assistant assigned to download their profiles. However, because research assistant assignments were random, this scenario would only be more likely to have occurred the more Facebook friends the given student had; and therefore the “degree effect” of Facebook friendships on privacy behavior can be expected to capture (and control for) much of this variation.
- 6.
The average within-neighborhood density at wave 1 is 0.076, compared to an average across-neighborhood density of 0.059. At wave 2, these numbers are 0.124 and 0.080 respectively; at wave 3, 0.150 and 0.091; and at wave 4, 0.166 and 0.100.
- 7.
All models were estimated using Siena’s unconditional moment estimation and the “initiative/confirmation” model type for undirected networks (Snijders et al. 2008; see also van de Bunt and Groenewegen 2007). This model type essentially simulates the process whereby Facebook friendships are actually created and dissolved: a tie is created if and only if one student “requests” a friendship and the other student then “accepts,” while a friendship can be terminated by either student. All models were run using five phase two subphases and 1,000 phase three iterations. Model convergence was in all cases excellent: the t-ratios for all parameters were less than 0.1 in absolute value.
- 8.
Technically, positive “network dynamics” coefficients refer to both the tendency for new ties to form and the tendency for old ties to be maintained; while negative coefficients refer to both the tendency for new ties not to form and the tendency for old ties to be deleted. Because friendship deletion is very rare in this network, however (Table 8.2), I focus only on the case of new tie formation for the remainder of my interpretation of results.
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Lewis, K. (2011). The Co-evolution of Social Network Ties and Online Privacy Behavior. In: Trepte, S., Reinecke, L. (eds) Privacy Online. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21521-6_8
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