Time-series analysis of blog and metaphor dynamics for event detection

Open source indicators (OSI) like social media are useful for detecting and forecasting the onset and progression of political events and mass movements such as elections and civil unrest. Recent work has led us to analyze metaphor usage in Latin American blogs to model such events. In addition to being rich in metaphorical usage, these data sources are heterogeneous with respect to their time-series behavior in terms of publication frequency and metaphor occurrence that make relative comparisons across sources difficult. We hypothesize that understanding these non-normal behaviors is a compulsory step toward improving analysis and forecasting ability. In this work, we discuss our blog data set in detail, and dissect the data along several key characteristics such as blog publication frequency, length, and metaphor usage. In particular, we focus on occurrence clustering: modeling variations in the incidence of both metaphors and blogs over time. We describe these variations in terms of the shape parameters of distributions estimated using maximum likelihood methods. We conclude that although there may be no “characteristic” behavior in the heterogeneity of the sources, we can form groups of blogs with similar behaviors to improve detection ability.