As of April , one in every eighteen United States citizens are using big data to find a companionship . In the age of online dating, big data analytics has become a major contributor to leading to potential relationship success, because online dating services have to deal with a huge amount of data. As an example, Match. This demonstrates that technology and big data are changing the dating game. Online dating sites use many methods to generate and collect data about their customers. Typically, most information is gathered through questionnaires . The questionnaires ask for likes, dislikes, interests, hobbies, and so on.
For the millions of people who use online dating platforms, potential dates, hook-ups or even love are now just a click or a swipe away.
How Big Data Changed Online Dating
The data we give away might seem like a small price to pay for the possibility of finding love, but what is happening in the background? How data is collected, analysed, sold, traded or reused might be more complicated than we think.
The data you give away when using dating apps might seem like a small price to pay for the possibility of meeting someone new. The systems by which data is collected, analysed, sold, traded and reused might be more complicated than you think. Personal data is the goose that lays the golden egg in our modern economy. The industry of data brokers—the ones who buy and sell our data to third parties—is facilitated by the companies that organise our lives with operating systems, apps and hardware.
But this field is a growing and lucrative business model that in the case of the dating game can include information you probably originally intended to reach very few people.
Tinder, for example, collects and stores the sensitive data of its 50 million users worldwide. Tinder also knows which kind of people are interested in you. All the information you give dating apps is shared with Match Group, Inc.Women on Dating Sites Cause Incels
After the allegations, OKCupid said they ceased to do it. Now think about Grindr, a majority gay men dating app with 3. The information requested, or that you deliver voluntarily to build up your profile, includes: If the data was released to partners, it may have been used as direct marketing or sold as a package of information to a data broker. Any information you provided to create your profile also exists in the form of a record held by a number of third parties.
They got pages of all her activity in the app, as well as apps connected to her social media profiles such as Facebook 8. According to Dehaye, the data collected by the app and shared with third parties is used for profiling and can affect your life when asking for a loan, applying for a job, a scholarship, or medical insurance.
In the case of Grindr, or other LGBT apps like Her, Gayromeo, etc, it could also be very dangerous if you end up trapped in an airport in one of the 10 countries that punish homosexuality with a death sentence. Aside from Facebook however, different dating apps allow connections to other social media platforms, specifically: Only one dating app, Belinked, relies on connection to LinkedIn without also connecting to Facebook.
In the bottom part of the map there are several dating apps — from Scruff to Elite Singles to Ashley Madison — that apparently do not connect to any social media platform at all. Instead, these dating apps seem to be quite isolated in the app ecology. To the left side of the map, there is a small cluster of Chinese dating apps, including Momo, that only connects to two Chinese social media platforms.
Seeing that Facebook, Instagram, Twitter, and other common platforms are not used in China, it makes sense that the Chinese dating app exists in an isolated ecology when it comes to social media connections.
By viewing network traffic we can identify which third parties the dating app connects to. In addition, if the connection is insecure, we can also capture which data points are being shared.
The Grindr app communicates with 13 different advertising hostnames, some of which are unencrypted HTTP requests that include plain text user info such as GPS coordinates and type of phone. It is not easy to determine what accounts are fake or real, for users and app administrators alike. In the example below, from an account created for testing purposes, you can see a typical unsecured HTTP connection to a third party advertiser.
Here, the phone's brand and model, operating system, mobile carrier, device, screen dimensions, Grindr app, gender, age and gps coordinates, are all identifiable:.
As part of an in-depth research project from which much of the information in this article was derived, Tactical Tech carried out 50 in-depth, qualitative interviews distributed around the Middle East and North Africa, Europe, the US and Latin American regions to users of dating apps from various genders, ages, and sexual orientations, some of which translated excerpts are presented below. However, one time, while being very conscious of not revealing any extra information, one guy was able to know my name, where I worked and the conferences at which I spoke , just by googling keywords of my first name and the college I went to.
It turns out that there I am the only person with that name who graduated from that college. Online dating sites use many methods to generate and collect data about their customers.
Typically, most information is gathered through questionnaires . The questionnaires ask for likes, dislikes, interests, hobbies, and so on. The number of questions asked depends on the service that the user has selected. It appears that the more successful sites ask hundreds of questions to get better results . Diagram shown in Figure 6 provided by an article  illustrates a simple depiction on how matches are made based on the information provided. In addition to questionnaires, some sites collect data about customers from social media accounts and online shopping history by asking for user permission to have access to those accounts .
This information allows online dating sites to observe the actions of its customers, not only what is filled out in a questionnaire .
After the site collects a large amount of data, the information is analyzed; all the data is compiled in a database system including RDBMS and NoSQL databases, and then sifted through using a variety of different algorithms to predict the best matches .
The main objective in online dating is to find accurate matches. However, it is debatable whether big data actually improves the chances of a potential soulmate. Those against big data in online dating claim that there is a high probability that both females and males may unintentionally or intentionally misrepresents themselves . This is a major weakness for online dating sites to overcome. This is done by obtaining their search history, shopping history, and profiles on social media sites.
Other professionals believe that big data is essential to finding the right relationship. The thought is that big data creates facts, and facts do not lie . These behaviors include where the customer likes to shop, what shows they watch on Netflix, what social media site they perform, and so on. Zhao from the University of Iowa has created a collaborative filtering system that looks at browsing behavior, in addition to responses from potential matches .
Examples of the browsing behavior are where does this person shop online and what music do they listen to. This particular algorithm for online dating works similarly to how Netflix and Amazon recommend certain products .
Almost every dating site has created their own algorithms using big data in order to create meticulous matches. The next two paragraphs will analyze big data techniques that eHarmony and Match. Every piece of information collected by eHarmony is used to determine each likely match for their users .
Data on dating sites
In addition to big data, eHarmony also utilizes machine learning to establish over one billion matches daily . The matchmaking system for eHarmony is built in MongoDB which allows matches to be made in under twelve hours .
Next, points are given to the user based on a variety of predetermined qualifications. For example, how important is it that your potential partner answers this question in a similar way ? Once the points have been assigned, users with similar points are matched together. Instead of using big data to create matches, Match. If distinct differences are found, the algorithm adjusts the match to create more accurate depiction of the user .