Social Media, built on Web 2.0 Technology, offers individuals an accessible and interactive environment. While Social Media users convey their feelings and thoughts through various platforms, they can receive support from others. Information and ideas are spreading faster than in any media environment before, and they can even turn into actions by being transferred from the virtual environment to the real world. The data produced in this media environment, which can have significant repercussions, is used in many areas today.

Although individuals differ according to the Social Media Platform they use, they play a role in the formation of Big Data by producing various types of data such as photos, videos, and location information. The processing of this data made by users has recently started to attract the attention of researchers in different disciplines. A new field called Social Media Mining is developing, and its applications are seen in various areas such as customer relationship management, public relations, product management, and advertising.

Social Media Mining, which consists of the processes of obtaining data from Social Media Platforms and preparing it for analysis with the help of various tools and interfaces, and analyzing the analysis findings with the use of interdisciplinary methods, is a technique that allows many researchers to carry out applications in different fields. Furthermore, with the development of social media and the diversification of Social Media Platforms, the analysis of the data produced and shared in this new media environment is gaining more importance daily.

Social Media data needs to be analyzed to obtain meaningful information from the large data pile that emerges with Social Media for both individual and corporate purposes.

The platforms where social media data is generated are a kind of living laboratory that allows the collection of large amounts of data in academic research. Social Media Data, which is used for analysis and practical solutions, is seen as an opportunity by organizations in advertising, Public Relations, Customer Relationship Management (CRM), and Business Intelligence (BI) targeting. Many corporate companies need to be used effectively, especially as an additional channel for marketing.

Social Media Platforms, which have grown in recent years in parallel with the increase in mobile internet usage, are an alternative to standard communication tools at many points.

The use of Social Media as a data source brings unique and new features.

How Does Social Data Mining Work?

In general, the Social Data Mining process involves Statistical Techniques, Mathematics, and Machine Learning (ML).

The first step is collecting and processing Social Data from different Social Media Sources. Besides Social Media Platforms such as Facebook, Twitter, or YouTube, data miners also pull data from various blogs, news sites, forums, or other public pages where users interact and leave comments. All this information must then be processed before moving on to the next step.

After data is collected and processed comes the application of various Data Mining Techniques that allow for easier identification of common patterns in large datasets and correlation of different data points.

Social Media Data Mining Techniques are Classification, Attribution, Tracking Habits, Predictive Analytics, Keyword Extraction, Sentiment Analysis, and Market/Trend Analysis.

In addition, Social Media Data Mining also uses several Social Media Data Mining software solutions to optimize the mining process. The best–known Data Mining Software Solutions are Microsoft SharePoint, Sisense, IBM Cognos, RapidMiner, and Dundas BI., provided that a more in–depth analysis of the data is needed.

The final step in the mining process is to create a visual representation of the insights from the whole process to deliver the information to the target audience. This is usually done using Social Media Analytics or various data visualization tools such as Infogram, ChartBlocks, Tableau, and Datawrapper.

The fact that Social Media Provides access to large amounts of data brings up the issue of Social Media Mining for companies.

Social Networks, which were previously used to communicate and socialize with the immediate environment, have expanded their sphere of influence and turned into environments that offer vast opportunities for brands. Social Media, where different topics are shared, national and world agendas are discussed, and educational and informative publications are shared, offer essential opportunities for brands in terms of marketing. In addition, advanced software and Artificial Intelligence (AI) technology are used to draw meaningful results from the vast data pool in Social Media.

Why is Data Mining Important in Social Networks?

Every content that people share on Social Media can have commercial value. Thanks to these contents, strategies can be developed to increase the customer experience by determining their preferences and interests.

Thanks to Social Media Mining, which is also expressed as Social Network Analysis, behaviors are predicted, marketing activities with high returns are carried out, and targets can be achieved.

Companies can predict their potential customers by Data Mining on Social Networks, creating high–return advertising campaigns, and making users feel special.

How is Social Media Mining Done?

Data Mining, which can be expressed as converting a lot of scattered data into meaningful information, provides benefits with sales forecasts and market basket analyses revealed by examining basic features such as age, education, gender, and location. Data Mining, a fundamental discipline in the marketing, banking, and insurance sectors can be widely used in almost every field.

With the widespread use of the Internet, Social Networks are expanding. Social Networks, which have become the first way to have fun, offer large amounts of data about large audiences.

The most widely used Data Mining Networks are; It stands out as Facebook, Instagram, Twitter, YouTube, and LinkedIn.

Applications that include various services such as instant messaging, video, e–mail, games, file, and photo sharing facilitate the interaction of users. Social Media Mining relies on extracting meaningful information from the data piles that emerge from this interaction. Thanks to the Data Mining Approach in Social Networks, meaningful data can be reached in academic, commercial, sociological, and many fields. Social Media Mining involves sourcing, information extraction and preprocessing, generalization and analysis.

The following are generally used as Data Mining Techniques in Social Media:

• Keyword Extraction (detection of frequently used words),
• Market/Trend Analysis (determining which direction the trend is),
• Social Spam (unauthorized and coercive spread of spam on social networks),
• Predictive Analytics (the process of using historical data to predict future trends),
• Sentiment Analysis (processes of calculating and identifying and classifying opinions/statements through various algorithms).