1. A computer-implemented method for analyzing social media events, comprising: gathering a plurality of social media data received from one or more social media sources, each social media data including a post; extracting one or more features associated with each social media data; identifying substantially similar features among the plurality of social media data; clustering the plurality of social media posts that share substantially similar features, thereby identifying one or more clustered features patterns; and detecting a burst of clustered social media posts that have similar characteristics, wherein the characteristics of each social media post are determined by the composition of the associated features; The detecting step comprises detecting the burst of clustered social media posts that have similar characteristics by the following equation: Storm ⁡ ( P j ) = { 1 if ⁢ ⁢ ∑ i = 1 n ⁢ g ⁡ ( f ⁡ ( T i , P j ) , δ j ) ≥ S j k 0 otherwise Wherein, the symbol Ti represents the vector representing tweet vector i, the symbol Pj represents the pattern vector for pattern j, the symbol f represents function that computes the similarity score of a message tweet Ti to a given reference pattern Pj, the symbol g represents a function that compares the similarity score, the result of f (Ti, Pj) of a message tweet Ti and Pj, to a threshold di in order to create a counting score to determine if a group of messages Ti comprise a storm, the symbol S j k represents the pattern specific threshold (or storm threshold, or shape function threshold) learned after k−1 iteration, the symbol δ represents a threshold for similarity of tweet to a referenced pattern, γ S j k and the symbol δ j represents a pattern specific similarity threshold value.