1. A method comprising: generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category, wherein generating the social network graph comprises: harvesting and aggregating posts of online social media conversations from one or more online social media sources; performing content scraping on the posts of online social media conversations to obtain raw data including each individual post of an online social media conversation relevant to the subject matter of interest and user-profile information of a social media author corresponding to each individual post; and performing social network analysis processing on the raw data to obtain the social network graph, the processing including: analyzing an influence level of the social media author, the influence level indicative of a total number of other authors the social media author is connected to over one or more social networking paradigms, a total number of posts written by the author in one or more social networking paradigms, an importance score associated with the one or more social networking paradigms in which the social media author's posts are published, a total number of distinct subgroups of authors the social media author is connected with, and an overall activity level of the social media author; computing an influence score associated with the social media author using an influence score formula, the influence score formula computed by multiplying a raw betweenness centrality value for the social media author with a function of: (1) a number of other active authors on the website where the social media author is active, (2) a total number of posts the social media author has contributed, and (3) one or more correction parameters that are fine-tuned for purposes of a specific vertical that corresponds to the specific subject matter of interest; wherein the betweenness centrality value is computed as a result of a centrality measure of a vertex within a raw social graph generated for the given social networking paradigm; detecting a sentiment rating associated with a one or more posts of the social media author; and determining weighting factors associated with the one or more posts based on a combination of the sentiment rating associated with the one or more posts and the influence level associated with the social media author.
3. The method of claim 1 , wherein harvesting the online social media conversations includes: performing forum analysis to discover websites of interest containing online social media content relevant to the subject matter of interest in the category; and searching through the websites of interest to extract social media conversations related to the subject matter of interest.