This work addresses the problem of detecting topic-based influencers in social media. For that end, we devise a novel behavioral model of authors and readers, where authors try to influence readers by generating "attractive" content, which is both relevant and unique, and readers can become authors themselves by further citing or referencing content made by other authors. The model is realized by means of a content-based citation graph, where nodes represent authors with their generated content and edges represent reader-to-author citations. To find the top influencers for a given topic, we first profile the content of authors (nodes) and citations (edges) and derive topic-based similarity scores to the topic, which further model the unique and relevant topic interests of users. We then present three different extensions of the Topic-Sensitive PageRank algorithm that exploit the similarity scores to find topic-based influencers. We evaluate our solution on a large real-world dataset that was gathered from Twitter by measuring information diffusion in social networks. We show that, overall, our methods outperform several state-of-the-art methods. This work further serves as an evidence that the topic uniqueness aspect in user interests within social media should be considered for the influencers detection task; this is in comparison to previous works that have solely focused on detecting topic-based influencers using the combination of link structure and topic-relevance.