There isn’t any denying that social media has become the major digital metropolis of our times. Twitter and Facebook alone make up a good portion of the world opining about virtually everything. In some cases, it also means setting precedents, something we’ve seen with the great organization in political groups.
Technology plays as much of a factor now in scoping out social media information. Machine learning can scan social channels to create preferences for specific types of media.
Take a look at how social media influences streaming media today and in setting content preferences for audiences.
How Does Social Media Opinion Influence Music Streaming?
Tech analysts continue to look at how much social media affects what you play on music streaming sites from Spotify to SoundCloud.
It’s not a new analysis when you consider the ability to create personal communities on social media was already around more than five years ago. Those options are even greater five years later, including the rise in media influencers and abilities to share information in real-time through live streaming apps.
All of this factors into one giant melting pot of opinion about music today. Trying to assess all that opinion, though, is impossible for even a large group of humans.
Only artificial intelligence and machine learning can tap into what’s said about music on social media and influence what you’ll hear on streaming music services.
Content Personalization on Music Streaming Services
If you’ve read up on machine learning recently, you’ve probably seen how much personalizing goes into entertainment and media. Thanks to A.I. programs being able to understand consumers better than they know themselves, more personalized recommendations can take place through media streaming.
In music streaming, you’re seeing this being used effectively at Pandora. The algorithms they use in machine learning work on a far deeper level beyond providing music recommendations from past behavior.
Their platform works by also extracting information about the emotions of songs and how they affect listeners. Known as the Music Genome Project, the Pandora team is made up of musicologists who understand the mechanics of music and how it resonates.
Using metadata from machine learning, Pandora can now provide more relevant music recommendations than any other service available. As eerie as this might sound, providing the music their users really want to hear is a powerful way to retain their user base.
Personalizing Streaming Video
Artificial intelligence works with streaming video as well. When machine learning programs scan social media, video companies can find out cumulative opinion on which TV shows or movies are being discussed. This can persuade leading streaming services like Netflix to decide which movies or TV shows they’ll promote over others.
Machine learning works equally on a more technical level particularly in preventing potential buffering lags. MIT Computer Science and Artificial Intelligence Lab (CSAIL) worked to create an AI program that prevents this from happening. They designed a neural network allowing a way to decide when a connection requires one particular algorithm over another.
It’s a way forward to prevent future buffering problems in video streaming services, which is good news as public demand increases.
The Future of More Targeted Marketing
Since streaming services are already mainstream and likely the main source of how we’ll view entertainment in the coming decade, machine learning is going to help with marketing. A.I. continues to evolve and help with this by working on social media channels. It results in clustering and knowing more detail about human behavior, bringing a way to mimic how specific demographics talk on social channels.
Creating a more personalized linguistic approach to streaming service marketing results in providing a truly customized experience no one’s seen before.
Visit our website to learn how we can use machine learning to scan social media and help influence what you provide in the way of streaming media.