- The number of followers you have on Twitter and number of friends on FaceBook.
- The number of links you provide, groups you join, retweets you make and how often you talk with other friends / followers.
- The number of re-tweets, FaceBook “likes”, comments and views that a blog post generates.
- The personal information you provide (such as Twitter Bio)
- The concepts being discussed in Tweets and FaceBook walls.
By applying Predictive Analytics to all of this information an impressive number of applications arises such as :
- Analysis of your Twitter Bio and words that are contained in your Tweets. For example we can identify what do people stating in their Bio being “Computer Geeks” discuss more frequently (in terms of Electronic Brands, technology trends etc). (See more here)
- Analyze thousands of Twitter accounts and find words that could make a difference in your follower count. (It appears that you should keep things positive -at least most of the time-. See why here).
- Identify best practices on how to use Social Media : When to post your new blog post, which words and concepts to avoid writing about and ultimately what concepts (such as Personal Branding) you should focus on. ( See more here).
- Understand consumer behavior : What people liked, how they feel and what they would like to see in upcoming products and/or experiences. See this example on how different aspects of consumer behavior in shopping malls is “mined”.
Note that these are just some examples. The list goes on.
There is no doubt that new exciting Social Media apps will become available. This in turn will produce even more Social Media data (such as ones that contain location information). Being able to combine Data Mining and Text Mining techniques to extract insights from Social Media Data will become a very important skill to have.