In this empirical study of online leadership, analysis for movie recommendations on Douban, one of the biggest interest-oriented online Chinese-language social net- working systems of its kind, we address the identification of the characteristics of key opinion leaders using a big data processing framework. As an illustrative case study, we focus on a niche subset of popular audience content on Douban: approximately a half million short comments regarding the top 94 most popular South Korean films produced between 2003 and 2012. Raw data samples, including film details, review comments, and user profiles, are harvested via one asynchronous scraping crawler, and then their heterogeneous features are manipulated accordingly. Finally, a parallel association rule-mining (ARM) algorithm is employed for revealing leadership patterns. The proposed framework explains how to extract high-level features that can then be used to gauge the effectiveness of these so-called key leaders and their ability to generate word-of-mouth (WOM) awareness and interest surrounding their recommendations. In turn, researchers can edge closer to determining the kind of charismatic ‘soft power’ appeal of leading reviewers and reviews that are facilitating among follower networks new opportunities to evaluate a film and ultimately to decide to view it.