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Department: Computer Science
Class Year: 2015
Abstract: Given the amount of information on the Internet and the competition between sites over users’ attention and dollars, many websites today are personalizing their content, in order to provide users with a more tailored experience. While in many cases, content personalization is highly beneficial to the user, personalization can also indicate a compromise in user privacy, stemming from the use of information the user did not explicitly provide and, in some cases, can involve the manipulation of content to the user’s disadvantage, as occurs in cases of price discrimination. While before, users only had to be concerned about information being used about their computers and their browsing and search histories, in recent years, a new source of detailed user information has emerged, social media. The social media site, Facebook, provides the opportunity for users to connect their social identities to many websites using Facebook’s social login button. When users connect to sites through the social login button, information from their profiles is shared with the websites. Given our knowledge of how sites use other user information for personalization, we must be similarly concerned with understanding how information coming from social media sites can be used by third-party sites for personalization. Unfortunately, the tools and techniques for detecting this type of personalization are underdeveloped, and no studies of social information based personalization have been conducted. In this paper, I make four contributions towards addressing this area of research. First, I develop a process to collect the page source data from websites, that is necessary for detecting personalization, and develop a large corpus of such data for 235 websites that allow Facebook social login. My second contribution is an algorithm for identifying content changes on websites that result from a user being logged in through Facebook. Third, I develop a taxonomy of social information based personalization, and identify four types of personalization that can occur as a result of using Facebook social login. I create dummy accounts that simulate user features on Facebook, and find evidence of Facebook-based personalization on numerous sites. Given the existence of personalization, the final contribution of this paper is a theoretical examination of techniques for reverse engineering Facebook-based personalization on websites.
Extent: 67 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2017

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