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This includes contact information shared by others, location data, and browsing history. But the social network also characterises users based on what pages they have liked, a key indicator of their interests.

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T hey found that fewer than eight likes were typically needed to distinguish between a person being confidently identified as one of these traits, and not identified. Such inferences are increasingly being used by advertisers as consumers become more wary of giving away information. T he researchers said many would find this unsettling. To many, privacy invasions via statistical inferences are at least as troublesome as privacy invasions based on personal data.

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T hey propose allowing Facebook users to prevent inferences based on certain likes. Agency News. Artificial Intelligence. Augmented Reality.

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

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

For example, MIT students might join the MIT and Boston networks and could therefore view and be viewed by other members of those networks. This default setting permitted us to access a significant number of profiles in the MIT network because we are members of the MIT network. Then specific people can be designated to see only the limited profile. For example, if Alyssa P. Hacker did not want Ben Bitdiddle to see her phone number, Alyssa could specifically hide her phone number in her limited profile and tell Facebook that Ben should be shown only this limited profile.

Alternatively, Alyssa could add Ben to a blocked list that prevents Ben from seeing anything about Alyssa or even that Alyssa has a Facebook profile.

Facebook can tell whether you're gay based on a few 'likes,' study says

It should be noted that Facebook users also have two additional options to secure their privacy: For instance, if Alyssa P. Hacker is uncomfortable with sharing her mobile phone number in her profile, she can either a leave the mobile phone number field blank or b list a bogus mobile phone number. Later sections of this paper question whether the privacy options discussed here provide adequate privacy safeguards or merely create an illusion of privacy.

Specifically, we will analyze how seemingly innocuous friendship associations reveal intimate details about Facebook users. Consider that children on the playground segregate themselves based on sex: One study showed that men had 65 percent male friends versus 35 percent female friends, while women had 70 percent female friends versus 30 percent male friends Reeder, Another study revealed that this phenomenon persists in lesbian, gay, and bisexual individuals [ 4 ].

In short, males prefer males for friendship, and females prefer females for friendship. Lesbians and gay men draw the majority of their friends from the LGB community while bisexual women and men draw the majority of their friends from the heterosexual community, as discovered by Paz Galupo in a recent study of close friendships and LGB individuals [ 5 ]. The study allowed participants to report on up to eight close friends, which is reasonable because of the limited human capacity to maintain close friends.

Supporting this notion of limited capacity, Malcolm Gladwell poses the following exercise in his book The Tipping Point: Of those 4. Galupo based her research on data from a larger study, and the heterosexual participants of that larger study reported an average of 4. These numbers are surprising, especially when viewed in the context of the frequency of homosexuality, as discussed below. A number of studies have attempted to determine the frequency of homosexuality, although these studies have struggled with the complexity of sexual orientation.

Is homosexuality defined by same—gender attraction, same—gender sexual activity, self—identification as LGB, or something else altogether? Studies have reported the frequency of homosexuality in several ways to capture the complexity of defining sexual orientation. For example, while Kinsey reported that 30 percent of the male population had incidental homosexual experiences or reactions at some point in their lives, only four percent of the male population was exclusively homosexual throughout their lives [ 11 ].

Because our research relies on public self—identification of same—gender interest in Facebook profiles as a sentinel value for LGB identity, Table 1 shows only frequency of homosexuality statistics with a high standard for determining homosexuality, such as same—gender sexual identity or primarily same—gender sexual activity. We assumed that Facebook users are unlikely to identify publicly a same—gender interest in their profiles because of a single homosexual experience, for example.

There are a number of possible explanations for such self—segregation phenomena. For example, people are already geographically separated, which therefore reduces opportunities for people to interact with others in different locales. Another theory is that individuals like to reinforce their self—identities, behaviors, and attitudes by associating with people similar to themselves. 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? 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.

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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.

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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.