It would be an extreme understatement to say social media is popular. Considering the hundreds of millions of people that regularly check their Facebook, Twitter, and Instagram accounts, it’s no stretch to say that social media has become a regular part of life. In fact, 63% of Facebook users log into the social networking site at least once a day. (Tweet This) Considering Facebook has about 1.28 billion users, that’s a lot of people. While there may be many reasons social media has gotten so ingrained into our lives and continues to grow in popularity, one of the major driving forces behind this growth is undoubtedly machine learning. As more social media sites utilize machine learning, they’ll see more dedicated users and increased likeability.
You’ve likely had some experience with machine learning’s effect on social media, even if you haven’t realized it. Check out your news feed on Facebook some time and you’ll see machine learning in action, or at least you’ll witness the results of what’s happening behind the scenes. Almost every action you take while on Facebook can be stored and analyzed to map out what makes you tick. Think about all the data Facebook is able to collect on who you are, what you like, what you dislike, and what drives you to act. Any time you “like” a post or article, that’s data Facebook can use. Any time you click on a link, post a status update, and upload photos and videos is even more data for the world’s most popular social media site to analyze. All that information goes into determining how to set up your news feed.
It wasn’t always this way. Years ago, if you may remember, Facebook would organize your news feed in chronological fashion. While this was adequate for the time, it often meant news feeds were cluttered with spam messages, uninteresting posts from casual friends, and link-bait headlines that only served as annoyances. Machine learning has changed how Facebook handles your news feed. Based upon your actions while on the site, machine learning crunches the data to customize your news feed into something you prefer. It’s no easy task; Facebook has to determine which posts you’ll be most interested in out of approximately 1,500 possibilities. To do this, more data is collected beyond your social media activities. Facebook also bases its algorithms off of A/B testing, surveys, and information on how much time you spend away from Facebook and what you do when you return.
This is all made possible because the machine learning used by social media sites (along with many other businesses) is considered to be “second generation.” The first generation of machine learning was fairly basic, using keywords to determine more about users’ behavior. With machine learning becoming more sophisticated and much more accurate, social network sites like Pinterest can be much more precise when organizing your homepage according to what you will want to see and read about.
Machine learning is also a major necessity since the amount of data is absolutely enormous. In pure statistics, one report shows Twitter deals with 12 times more data each day than the New York Stock Exchange, while Facebook tackles even more--500 times what the NYSE has. This requires new storage capabilities, like those available through flash storage (and if you’re asking, “What is flash storage?” you can check out this site for more information). One of the major challenges is trying to capture and classify all that data, especially since roughly 90% of the data is not spontaneously generated, or “unstructured.” Machine learning is helping immensely with that process by engaging in high-level thought and abstractions in order to make sense of it all.
One example of this intensive machine learning is how Facebook hopes to analyze photos. Since users upload 350 million photos each day, Facebook is looking to use machine learning and artificial intelligence to identify objects and people within the photo. If you’ve uploaded a photo recently and noticed that Facebook automatically tagged you or a friend, that’s the result of machine learning. The same idea would likely apply to videos. And since machine learning is a continual process that constantly builds on more data sets, the ability to accurately identify people and objects would only get better with time.
Social media is on the forefront of using machine learning to make each user’s site more personal. The more it appeals to the individual, the more value users will see in it. While issues like privacy still need to be addressed, machine learning appears to be here to stay.
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