Facebook is gay

Regardless of their origins, homophily patterns are likely self—sustaining because family, friends, and other associates exert pressure to conform to their norms [ 12 ]. Taking a step back, if equal status contact is such a persistent empirical regularity in social relationships, how might such self—segregation manifest itself in online social relationships?

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For humans, the social channel capacity is about people. A phenomenon called the principle of locality heavily biases friendship formation in the real world based on proximity. According to Mikolaj Jan Piskorski at Harvard: If Facebook really does map to real—world social relationships, what might we learn from analyzing those friendship connections? The researchers built a social graph from a large corpus of e—mail messages, and detected individuals who may have been alienated or had a hidden agenda.

If data mining can reveal hidden relationships, what might data mining reveal from a large corpus of data from a social networking site like Facebook? Real—world self—segregation should carry over into online social networks. Because males have more male friends, and LGB individuals draw many of their friends from the LGB community, one would expect gay males on Facebook to have a higher proportion of gay male friends than heterosexual males.

Because females have more female friends, and LGB individuals draw many of their friends from the LGB community, one would expect lesbians on Facebook to have more lesbian friends than heterosexual females. Why focus on sexual orientation? Firstly, from a technical perspective sex and sexual orientation data is easy to access. Note that Facebook does not adequately support the complexity of human sex and sexual orientation, and therefore certain subjects, such as transgender identities, cannot be addressed by our study.

The other fields in a Facebook profile are editable text fields that maintain no invariants to restrict the contents of the field. According to one study: The ability to detect such characteristics without physically observing a subject introduces a new threat to privacy. If we could indeed find a strong correlation between friendships and sexual orientation, it would represent a significant privacy risk because network data — data that relates one user to another — is not generally considered sensitive information and is afforded little protection under the Fourth Amendment in the U.

For example, although a warrant is required to obtain a wiretap, a warrant is not required to log telephone numbers dialed Smith v. Maryland , Sexual orientation is impossible to manipulate as an experimental variable, so designing an experiment to determine whether a causal relationship exists between sexual orientation and friendship is no easy task. We conducted a correlational study using archival data recorded by Facebook.

Such an experimental design has a major shortcoming: Individuals were not included in our study for the following reasons only: After filtering by sub—network and detecting abandons, our dataset comprised Facebook profiles of 6, students associated with MIT. Of these, 4, disclosed their sex and these are broken down in Table 2. Our subjects were 42 percent male, 25 percent female, and 32 percent unreported.

For comparison, Table 3 contains statistics on the entire MIT student population during fall when we collected our data. The sheer volume of data we wished to gather required automated collection of profile and friend information from Facebook. Our spider, called Arachne, a signed into Facebook, b received cookies from Facebook, and c downloaded Web pages with profile and friend information for each member of the MIT network.

Because we were downloading thousands of pages from Facebook, Arachne ran continuously from 24—29 October, 31 October—5 November, and 7—12 November Consider Facebook to be a large social graph. Each user is a vertex of that graph and friendship between two users is an edge. Assuming that the MIT network is a connected graph, it should be possible to traverse the entire graph from any starting point.

Essentially, Arachne performed a breadth—first search on the graph, starting with Facebook id , the id of one of the authors, as the root of the search tree. It should be noted that Arachne was conditioned to search for URLs embedded in Facebook friendship lists related to messaging. In order for Arachne to have catalogued a Facebook user, that user must have: See Figure 2 for an example of a Facebook profile that Arachne could not catalogue.

A cursory analysis suggested that such anti—messaging privacy settings screened no more than two percent of profiles. Implicit friendships provide the basis for detecting the sexual orientation of individuals who make their Facebook profiles and friendship associations private. Suppose that Alyssa P.

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Hacker and Ben Bitdiddle are Facebook friends. As a third party, one cannot see that Alyssa is friends with Ben, although one can see that Ben is friends with Alyssa. Figure 3 visualizes this implicit friendship.

Although many Facebook users have hundreds of friends and 45 percent of users return to the site daily Facebook, , a small fraction of Facebook users have only a few friends and have likely abandoned their profiles. To prevent users with few friends from skewing the results of our study, we implemented an abandonment detection algorithm.

Yes, Facebook probably knows you’re gay—but it doesn’t have to

Upon creating a new account, a new Facebook user has zero friends. Adding friends is a multistep process, and adding a large number of friends is time—consuming. The possibility of abandonment declines as the number of friends, and therefore the time investment, increases. Robert Cialdini in his book Influence: Once a person commits to something, even if the commitment is small, he is more likely to be consistent and stick to that commitment.

The time investment of adding friends on Facebook is an act of commitment, which reduces the likelihood that a user abandons his account. What might be the commitment threshold for Facebook users? And so he is committed. We elected to use this 12—friend threshold to detect abandonment, because friend data are usually available, even for private profiles, due to implicit friendships.

Just a handful of Facebook likes can tell if you are gay or straight, study says

Our abandonment algorithm removed all profiles with less than 12 friends in the MIT network from our directed graph model. Because the edge counts of the remaining nodes were reduced by this removal, the process was iteratively repeated until the number of nodes in the graph remained unchanged.

We analyzed the friends of users who self—reported in each of these sex orientation groups by finding the percentage of friends that fell into each sex orientation group. A logistic regression model produces a risk score, [0, 1], where 1 implies the input has the attribute the model tests for, and 0 implies the input does not. To build our logistic regression model, we inputted all subjects that self—reported as gay male and all subjects that self—reported as bisexual or heterosexual male. To decide on a threshold and evaluate the tradeoff between sensitivity and specificity, we plotted a Receiver Operating Curve ROC.

The ROC curve plots sensitivity as the dependent variable versus one minus specificity as the independent variable and is evaluated by the Area under the Curve AUC , which measures how accurately subjects are ranked by their risk score. An AUC of 0. For each user who self—reported in one of the sex orientation groups mentioned in the Statistical methods section, we found the percentage of friends that fell into each sex orientation group.

We then averaged these percentages over the entire sex orientation group. Table 4 provides a summary of these statistics. Figure 4 shows a subset of the data in Table 4, highlighting the percentage of LGB friends per sex orientation group and revealing that a gay male has, on average, a much higher percentage of gay male friends than the other groups. As can be seen from these data, heterosexual males have 0. Recall that friendship associations of Facebook users with private profiles can be determined implicitly.

A strong correlation exists between explicit and implicit friendship associations on Facebook. Across subjects with public profiles, the average number of explicit friends was Implicit and explicit friends are highly correlated. Figure 5 shows the ROC curve of a logistic regression model generated from data on all MIT Facebook users that self—reported as gay male and all MIT Facebook users that self—reported as bisexual male or heterosexual male.

The circle at the elbow of the ROC curve in Figure 5 indicates the sensitivity—specificity pairing we chose from which to derive our threshold value for our logistic regression. The threshold value that we arrived at was 1.

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The threshold value corresponds to a sensitivity of 0. It should be noted that the costs of misclassification might be asymmetrical. For example, the error of misclassifying a Facebook user as a homosexual male might be perceived as more serious in certain contexts than misclassifying a user as a heterosexual male.

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While the results shown in Figure 4 are interesting, the purpose of this research was to test whether one can predict information that a user keeps private. To test this hypothesis, we created a validation dataset of subjects who we knew to be gay male, as a privilege of our real—world acquaintances with them. Table 5 reports the results of analyzing the friends of these subjects.

Our results show strong predictive power for the sexual orientation of male MIT Facebook users. Table 5 shows statistics for members of our validation dataset, composed exclusively of gay males known to the authors a priori ; all had between 3. Such numbers are remarkably high and indeed were high enough for our logistic regression classifier to categorize these individuals correctly.

We were only able to obtain sensitivity and specificity numbers from those that self—identified a sex and sexual orientation in their Facebook profile. Notice the members of our validation dataset whose profiles were private; the data is quite sobering. Without any information about a Facebook user beyond a list of his friends, one can accurately predict his sexual orientation. As to why our hypothesis does not hold for lesbians, two possibilities are: This is a topic for future research.

Despite such a strong correlation, it is difficult to eliminate all threats to the validity of our study. While the validation dataset provides a startling example of how this research violates the privacy of individuals who try to make their Facebook profiles private, it was derived from individuals known to the authors to be gay males a priori. We consider this to be the single greatest source of selection bias in our study.

We limited our subjects to current MIT students, which introduced selection bias into our study. For one, we inherited the selection process of the MIT admissions committee.