Social media has become very popular and mainstream, leading to an abundance of content. This wealth of content contains many interactions and conversations that can be analyzed for a variety of information. One such type of information is analyzing the roles people take in a conversation. Detecting influencers, one such role, can be useful for political campaigning, successful advertisement strategies, and detecting terrorist leaders. We explore influence in discussion forums, weblogs, and micro-blogs through the development of learned language analysis components to recognize known indicators of influence. Our components are author traits, agreement, claims, argumentation, persuasion, credibility, and certain dialog patterns. Each of these components ismotivated by social science through Robert Cialdini's "Weapons of Influence" [Cialdini 2007]. We classify influencers across five online genres and analyze which features are most indicative of influencers in each genre. First, we describe a rich suite of features that were generated using each of the system components. Then, we describe our experiments and results, including using domain adaptation to exploit the data from multiple online genres.