Providing content and experiences to users in order to influence their thoughts and opinions is essential in many fields such as marketing, education, and politics. With advances and the growing availability of the Internet and Web technologies, posting documents online has become an effective means of communicating to large audiences. However, given individuals' different respective interests, characteristics, and abilities, shared documents will not likely be equally persuasive to all users. To communicate persuasively, the categorization of users according to factors like age, marital status, education, and occupation is becoming increasingly prevalent, as is providing users with content specific to their categories. This approach assumes individuals within a category are similar, which is not necessarily true. Persuasive communication should recognize differences at the individual level and personalize—rather than categorize—the content. However, a systematic software solution that tailors and prepares separate persuasive content for individuals does not yet exist. Given the increasing popularity and usage of social media, social-network extractable data can potentially provide a tremendous source of insight and background about individuals. Inspired by the Yale Attitude Change approach, this thesis proposes a multi-layer model called Pyramid of Individualization and the related software framework to generate persuasive content based on initial author input and audience social-media data. Preliminary results show the proposed system can create personalized information that (a) matches reader interests (attention), (b) is tailored to reader ability to understand the information (comprehension), and (c) is supported by a trustable source (acceptance).