Every minute, Facebook receives 3.3 million new posts, nearly half a million tweets are sent out, and 500 hours of video are uploaded to YouTube. It would be impossible for any team to view and analyze so much content. This is where machine learning comes into play.
Machine Learning in Social Media
Machine learning is a kind of artificial intelligence that uses algorithms to organize data patterns into clusters. With all social media platforms being constantly flooded with information, machine learning is the most efficient way to sift through and classify information.
Have you ever noticed that when you “like” or “share” a certain kind of Facebook post frequently, similar posts pop up more often? This is because Facebook uses an algorithm to show you more content and advertisements they think you’ll enjoy. Most social media platforms use similar algorithms.
Impact on Social Media Marketing
Machine learning has improved social media analysis for marketing purposes. Brands and marketers use information provided by algorithms to reach their intended audiences through social media. Companies can see when their brand is mentioned, and even whether it was mentioned in a positive or negative context. Machine learning technology helps brands and marketers connect with their audience and encourage content sharing in the most effective way.
Through machine learning, companies can collect valuable data about their target audience’s opinions and perceptions. They can see why different groups of people make certain decisions and analyze the buying cycle for their product. Using this technology allows brands to improve their products and services and better convey messages to their consumers. Companies can also pinpoint influencers, users with a high following in a specific niche, who they can partner with to create viral content for their brand.
Limits of Artificial Intelligence
It is important to understand that artificial intelligence has come a long way, but it is not perfect in terms of metacommunication, such as sarcasm and emoticons. For example, if a person tweets, “Wow, I LOVE being cut off in traffic,” it could be incorrectly classified as a positive statement. For a brand, algorithm errors like this could lead them to unwittingly ignore a dissatisfied customer or client. Avoiding these errors requires particular attention during the calibration phase.
Even with room for improvement, artificial intelligence and machine learning have come a long way in recent years. The technology has helped shape social media into the helpful tool we have learned to love. Machine learning will undoubtedly continue to amaze us in the near future.