The proliferation of real-time information on social media opens up unprecedented opportunities for situation awareness that arise from extracting unfolding physical events from their social media footprints. The paper describes experiences with a new social media analysis toolkit for detecting and tracking such physical events. A key advantage of the explored analysis algorithms is that they require no prior training, and as such can operate out-of-the-box on new languages, dialects, jargon, and application domains (where by "new", we mean new to the machine), including detection of protests, natural disasters, acts of terror, accidents, and other disruptions. By running the toolkit over a period of time, patterns and anomalies are also detected that offer additional insights and understanding. Through analysis of contemporary political, military, and natural disaster events, the work explores the limits of the training-free approach and demonstrates promise and applicability.
Prasanna Giridhar, Jongdeog Lee, Tarek Abdelzaher, and Lance Kaplan, "The event tracking dashboard: from multilingual social media feeds to event patterns and anomalies," Proc. SPIE 10653, Next-Generation Analyst VI, 106530V (Presented at SPIE Defense + Security: April 17, 2018; Published: 24 May 2018); https://doi.org/10.1117/12.2306712.
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