Embodying a new gestalt in firm-customer communication, social media are a nascent yet critical concern for researchers and practitioners alike. To date, very little research has accumulated in this area. The research community requires valid and reliable measures for social media effects in an organizational context, as do firms. Without such measures, firms remain unable to align their social media initiatives with organizational goals and ultimately create business value. This three-manuscript dissertation contributes a general framework for studying social media. Paper One presents a social media ecosystem model and focuses on the customer/firm segment entitled the B@C Social Media Dyad to provide a theoretical understanding of what firms and customers accomplish using social media. Paper Two further reviews the state of the art of textual analysis, a technique that can provide the deep level of qualitative analysis needed to fully ascertain important tends in firm/customer and customer/customer social media exchange, and concludes with the articulation of a set of design principles for developing a social media analytics system based on natural language processing capabilities. In Paper Three, the proposed approach is tested experimentally against sentiment analysis and manual approaches to mining knowledge from social media data and is demonstrated to provide superior support for organizational decision-making through improved problem detection. Of particular consequence is that accuracy of problem and opportunity detection is far greater given an NLP-based approach, while sentiment analysis appears no more useful than randomly reading segments of social media data manually. These results support our recommendations for a more useful system for monitoring firm-level effects of social media. As a whole, this dissertation enlarges our meager theoretical understanding of the role social media play in an organizational context and presents the research community with a solid foundation for pursuing subsequent inquiries into a variety of social-mediated outcomes. Further, it contributes to IS research by offering an information system intended to solve the organizational dilemma of how to derive meaningful knowledge from social media exchanges.