Machine Learning in Social Media

In 2019, Social Media is an important part of the technology industry worldwide. Currently, 3.5 billion active users are on social media platforms every month. To put it to perspective, 48% of the world’s population. This type of presence and impact is valuable. The companies that can take advantage of social media platforms will see increased success. In this article, we will explore the impact of machine learning on social media.

Social Media Data Analysis Explained

Social Media Analytics helps companies secure an advantage by knowing what their brand means to consumers. It involves an understanding of how users engage with particular products and services. Including the issues faced by users and getting to know the customer’s views about a product.

Consumers have the potential to present practical solutions for some issues companies may face. For example, for a product in the market without good analysis procedures, the chances of errors are higher. Users can provide solutions to these problems through methods like trial-and-error. This helps the company determine the requirements for better analysis. Which, in turn, helps users have a seamless experience.
SMA can be beneficial in helping companies improve their products and services. There are numerous posts highlighting complaints regarding various company’s products and services. This information contains sentiments that can be useful when viewing users’ experiences with a specific service. The information gathered from users can be analyzed to create a more seamless experience for users.

Also Read: How To Select The Best Heavy Duty Swing Set For Adults And For Babies?

However, this has been the case for media in general but more significantly in social media. In doing so, companies have been able to speculate and analyze what might interest a user and adapt marketing and advertisement algorithms to cater to that specific user.  Although it has worked, the amount of traffic to social media platforms makes it impossible to keep track of user activity accurately. The impact of this has resulted in two options, the employment of more labor or investment in new ways of tackling this issue. The latter is what will be the focus of this article. Machine learning, deep learning, and artificial intelligence are fields that social media marketing departments are funneling billions of dollars. Why? Because of the potential to effectively analyze and provide the appropriate data for specific unique users.

Trends in Social Media Analysis

In regards to the use of Artificial Intelligence in Social Media, Facebook has spearheaded the shaping of the enormous landscape of social media. And it continues to do so by meeting the expectations and needs of its consumers. The platform boasts of 2.32 billion active users, which makes it the most used social media platform worldwide. 68% of U.S. adults are active Facebook users. Facebook was also the first-ever social media platform to surpass the one billion active user mark, reaching this milestone in quarter 3 of 2012.

Emarketer has analyzed social media usage, and the information presented by the results according to each generation is astonishing. To break it down, 90.4% of Millennials, 77.5% of Generation X, and 48.2% of Baby Boomers are active users of social media Source: Emarketer, 2019.


So far, the portrayal of artificial intelligence in media has been towards machines completely thinking and behaving in a manner that highlights human characteristics. However, this is not the case (for now). AI isn’t used on a generalized scale. At least not yet. Therefore, such AI does not exist.

Instead, the Artificial Intelligence systems used by companies constitute applied Artificial Intelligence and Machine Learning systems. Applied AI works best when it is programmed for a specific task. The programming of this AI to understand and learn the correct procedure for handling a type of data makes it adapt over time and improves its speed and accuracy.
With machine learning, statistical techniques help us teach computers to learn without needing to make use of a rigid set of rules. To do so, the AI is shown several examples over time. From a few hundred to several million to our system until it eventually starts to learn and adapt over time and give more precise results and predictions.

Also Read: IT Support Services That Power Digital Expansion for SME’s

To put this in perspective, it will take the average professional 20 seconds to go through clicking ten pictures to determine which of the images are dogs and which aren’t. However, a machine learning program during its first stage of execution might take 20 minutes to perform the same task. But, after doing this for a day and adapting its speed and analysis, it could take the machine learning program less than a second to perform that same task. Even more accurately and efficiently pending the number of times it has been given that task to perform.

Growth and Adaptation of Machine Learning

In the Business Intelligence & analytics market, Data Science platforms that provide support for machine learning are predicted to grow at a 13% compound annual growth rate through 2021. Data Science platforms will surpass the bigger Business Intelligence & analytics market, which is predicted to grow at an 8% compound annual growth rate in the same period. Data Science platforms will increase in value from $3 Billion in 2017 to $4.8 Billion in 2021. Source: An Investors’ Guide to Artificial Intelligence, J.P Morgan. November 27, 2017.
Figure: Data Science Platforms are growing faster than overall BI & Analytics SW
In the year 2016, according to a study done by McKinsey Global Institute, research indicated that Machine Learning constituted around 60% of the total external investment of approximately $8 billion to $12 billion towards AI. What this means is that the competition to grasp the most advanced technology to keep track of user data is being taken seriously by companies. Among those at the forefront of research and development of machine learning is the social media giant, Facebook.

Also Read: Will Covid-19 Lead to More Outsourced IT Support?

Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all licenses granted. IBM, LinkedIn, Facebook, Intel, Microsoft, Google, and Fujitsu were the seven biggest ML patent producers in 2017. Source: IFI Claims Patent Services (Patent Analytics) 8 Fastest-Growing Technologies SlideShare Presentation.
Machine Learning Growth Rate Diagram

Future Predictions for Machine Learning and Social Media

According to predictions, Machine Learning and Artificial Intelligence systems will soon manage the way conversations and content flows entirely. These systems use filters to monitor and administer the comments of users across several social media platforms. They observe and report emerging crises before they spread out too far.

The idea is not to delete all negative comments to pull the wool over users’ eyes. Instead, when there is nothing inappropriate or assault about a comment, you should release official statements to look transparent. What this does is that it gives consumers the idea that their opinion matters. When customers send their queries, you can send them personalized messages using bots and machine learning systems.
In the modern digital age, determine the strength of a brand by how effective its social media management is. In the same way, we judge the success of a brand by its social media following and content.

Another aspect of machine learning is its ability to analyze and work with a vast array of languages without a need to reconfigure the data completely. Machine learning algorithms make use of clusters, which means that they can interpret multiple languages without changing the code that is important to the data. Furthermore, social media monitoring tools that make use of machine learning are helpful in the analysis of data. Such as, if you have non-native speakers of English within your target audience.

Because people that don’t speak English as their mother tongue tend to be vague in their communication, they can be hard to interpret and understand. However, machine learning makes this process easier by effectively taking text and emoticons and converting them into messages. Global audiences and non-native users of a language can then easily understand the information of the words.


In conclusion, machine learning is an area of technology that is expensive to harness. However, when successfully integrated, its advantages are undeniable. It is evident by the numerous investments made by social media companies toward machine learning. The effect and impact will continue to grow exponentially in the coming years, as highlighted in this article. Users can surely expect more catered and considerate content for their consumption in the future.