What is social media data mining and how it works?

What is social media data mining?

The term "social media data mining" refers to the process of extracting information or data from social media. Unlike conventional data mining, social media data mining explores beyond the internal databases and systems of a particular company or research firm.

In this process, data from a variety of social media platforms (e.g., Facebook, Instagram, Twitter, TikTok, LinkedIn, YouTube, etc.) is typically used to discover patterns and trends, draw conclusions, and provide insightful and actionable information.

How does social data mining work?

In general, the process of mining social data involves a combination of statistical techniques, math, and machine learning.

The first step is to collect and process social data from different social media sources. This data is publicly available and may include age, gender, ethnicity, geographic location, occupation, schools you have attended, languages you speak, friends and connections, networks you belong to, and more. In addition to social media platforms such as Facebook, Twitter or YouTube, data miners extract data from various blogs, news sites, forums or any other public page where users interact and leave comments. All of this information must be processed before moving on to the next step.

After collecting and processing the data, the next step is the application of various data mining techniques that make it easier to identify common patterns in large data sets and correlations between various data points. Some of the more commonly used social media data mining techniques include classification, correlation, tracking patterns, predictive analytics, keyword extraction, sentiment analysis, and market/trend analysis.

In addition, social media data mining employs many social media data mining software solutions to optimize the mining process. Some of the most well-known data mining software solutions include Microsoft SharePoint, Sisense, IBM Cognos, RapidMiner, and Dundas BI. If a more in-depth examination of the data is required, the data miner may also decide to use machine learning in this process.

Finally, all of these insights need to be visualized in some way in order to get the message out to the target audience. This is often done by using social media analytics or various data visualization tools such as Knowlesys Intelligence System, Infogram, ChartBlocks, Tableau, and Datawrapper.