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Public information about one's coworkers, friends, family, and acquaintances, as well as one's associations with them, implicitly reveals private information. Social-networking websites, e-mail, instant messaging, telephone, and VoIP are all technologies steeped in network data—data relating one person to another. Network data shifts the locus of information control away from individuals, as the individual's traditional and absolute discretion is replaced by that of his social-network. Our research demonstrates a method for accurately predicting the sexual orientation of Facebook users by analyzing friendship associations. After analyzing 4,080 Facebook profiles from the MIT network, we determined that the percentage of a given user's friends who self-identify as gay male is strongly correlated with the sexual orientation of that user, and we developed a logistic regression classifier with strong predictive power. Although we studied Facebook friendship ties, network data is pervasive in the broader context of computer-mediated communication, raising significant privacy issues for communication technologies to which there are no neat solutions.
Jernigan et al. (Fri,) studied this question.