Social media networks such as Facebook, Twitter, and Google Plus have experienced exponential growth in recently years as web-based communication platforms. Hundreds of millions of people are using various forms of social media networks every day to communicate and stay connected with each other. Consequently, the resulting activities from the users on the social media networks, such as tweets posted on Twitter, becomes phenomenal and can be collected for various kinds of measurements and analysis. Specifically, these user activity data can be retrieved from the social data sources of the social networks through their respective publicly available Application Programming Interfaces (APIs), indexed, processed, and stored locally for further analysis.
These stream data from the social networks collected in real time along with those collected and stored overtime provide the basis for a variety of measurements and analysis. Some of the metrics for measurements and analysis include but are not limited to:                Number of mentions—Total number of mentions for a keyword, term or link;        Number of mentions by influencers—Total number of mentions for a keyword, term or link by an influential user;        Number of mentions by significant posts—Total number of mentions for a keyword, term or link by tweets that have been retweeted or contain a link;        Velocity—The extent to which a keyword, term or link is “taking off” in the preceding time windows (e.g., seven days).        
In addition to the above measurements and analysis performed on the content of the data, it is also important to analyze the aggregated sentiments of the users expressed through their activities (e.g., Tweets and posts) on the social networks as well. For a non-limiting example, such aggregated sentiments can be measured by the percentage of tweets expressed by a group of users on the certain topic over a certain period of time that are positive, neutral and negative. Although such measurement of the sentiments of the users expressed over the social networks provide real-time gauges of their views/opinions, such measurement may be biased due to various factors, including but not limited to, the type of users most active and thus most likely to express their feelings on the social networks, timing and preferred way of expression by each individual user, etc. Consequently, as measured, the sentiments of users expressed on the social networks on certain matters or events may not be a true and accurate reflection of the sentiments of the public at large.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent upon a reading of the specification and a study of the drawings.