An author-reader influence model for detecting topic-based influencers in social media

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 ``\emph{attractive}" content, which is both \emph{relevant} and \emph{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.