Fifteen years ago, social networks didn’t exist. Today, more than 1 billion people log in to Facebook more or less regularly to share articles, photos and connect with “friends.” Through this “living” information archive and others like it, social networks provide an unprecedented volume and variety of both personal and impersonal information that can be a valuable, if messy goldmine of intelligence.
The practice of data mining collects and processes of unstructured information (things such as posts, comments, tweets, images) shared on networks like Facebook and Twitter.
The information collected may be used in many different ways, such as for identifying current and future trends, creating social profiles, capturing consumer insights or for creating a rich knowledge base from users’ clicks users across the web. By analysing the data in real time, social media data mining can also contribute to more sophisticated predictive modeling.
Data mining powered by AI and cognitive technologies can provide even more powerful intelligence from the information gathered from social media. The key is its understanding of language, meaning and context.
Data mining software is being applied in many areas. Companies, political parties, social and religious groups and others exploit the conversations and comments shared on social networks to gather information and intelligence to fuel research on markets, competitors, customers, competitors and more.