A Reaction-Based Approach to Information Cascade Analysis

Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless information cascades across many subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider the question: how do the many types of conversations within an information cascade characterize the process as a whole? In this paper we analyze the underlying features of the dynamics of communication, and use those features to explain the inherent properties of the encompassing information cascade. Utilizing "microscopic" trends to describe "macroscopic" phenomena, we set a paradigm for analyzing information dissemination through the individual user interactions that sprout from a source topic, instead of trying to interpret the emergent patterns themselves. This paradigm yields a set of unique tools for a myriad of application in the field of information cascade analysis: from topic classification of sources to time-series forecasting. We use these tools in a 88-million-row dataset for Reddit to show their conceptual effectiveness and accuracy when compared to the ground truth.