Social media platforms are leveraging data to increase user interaction and much more.

You might have come across personalized birthday videos while browsing through your social media account. How do you think these sites are curating these? They don't have to try hard for it since there is an abundance of data on the internet and data science behind it.

Data is the currency of the current digital world. Businesses have been leveraging data analytics to enhance growth and understand moving trends. According to the Markets and Markets report, the data science market is expected to reach USD 140.9 billion by 2024 and the growth will be driven by the increasing focus of enterprises on data-driven business intelligence.

Social media platforms are leading in this technique of using data and advanced analytics to provide a unique experience to their users. Social media users are growing in abundance with the high smartphone penetration rates, and this, in turn, increases the amount of data shared online. Let us understand how the two social media giants, Twitter and Facebook are leveraging data science to flourish and grow their business.

Social Media And User Data

The continuous flow of data on the social media platforms might create a ruckus since these are mostly unstructured data. Social media leverage data science to enhance user-experience and provide targeted and personalized content. The behavioral, personal, and demographic data that we share on our social media accounts do not go in vain. These media platforms leverage it along with the other posts and activities on the user accounts to track user behavior.

Apart from textual analysis, these platforms also use data science technologies for targeted marketing and advertisements. Big data and data mining are significant parts of social media analytics.

Facebook and Instagram Leveraging Data Science

Processing huge amounts of unstructured data to deliver intelligent insights is not an easy task. Data science using deep learning and machine learning is aiding this process, especially for social media giants like Facebook.

Facebook's DeepText and DeepFace algorithm analyses the user data and images to extract meanings from them. DeepText, an in-house product of Facebook, leverages loads of texts contextually to interpret them. Deep neural networks and NLP are used to establish correlations between the words. This tool can be used to suggest videos, posts, or other content based on the user interaction on the platform. DeepFace, another brainchild of Facebook is a tool that can efficiently recognize people in photos and uses facial recognition technology. Facebook fell into several controversies due to DeepFace since it raised questions about privacy infringements and data security.

As mentioned above, targeted content and ads are a benefit of leveraging data science. Facebook using data analytics to enhance personalization is no more a secret. Increasing traction and engagement is the prime focus of most businesses. Targeted advertising and content marketing using data analytics will enhance the user experience by customizing it according to their needs and behavior pattern.

Instagram, by Facebook, is another leading social media space with increasing users day by day. The visual appeal and aesthetics have attracted many users to Insta- as millennials call it. The platform uses DeepText and DeepFace tools developed by Facebook and amazingly leverages the capabilities of data analytics. Instagram takes help from DeepText to detect and filter spam messages.  Using a machine learning technique known as wonder embedding, Instagram deciphers user interaction with pages and categorizes similar content and pages to provide recommendations. You would have noticed how ads appear while browsing through Insta stories. These are targeted ads that are based on the train we leave online. Being connected to Facebook, Instagram now has a wide range of data and insights. Instagram's algorithm has been talked about a lot. It provides a personalized feed with relevant content that the users might share or like.

To remain at the competitive edge, digital media platforms are engaging in maximum utilization of disruptive technologies. Advanced data analytics, AI, and machine learning top the list. The development of these technologies to analyze human sentiments might open new avenues for data science and analytics.