Civil Unrest Prediction: A Tumblr-Based Exploration
This work focuses on detecting emerging civil unrest events by analyzing massive micro-blogging streams. Specifically, we propose an early detection system consisting of a novel cascade of text-based filters to identify civil unrest event posts based on their topics, times and locations. In contrast to the model-based prediction approaches, our method is purely extractive as it detects relevant posts from massive volumes of data directly. We design and implement such a system in a distributed framework for scalable processing of real world data streams. Subsequently, a large-scale experiment is carried out on our system with the entire dataset from Tumblr for three consecutive months. Experimental result indicates that the simple filter-based method provides an efficient and effective way to identify posts related to real world civil unrest events. While similar tasks have been investigated in different social media platforms (e.g., Twitter), little work has been done for Tumblr despite its popularity. Our analysis on the data also shed light on the collective micr-oblogging patterns of Tumblr.
KeywordsEarly Event Detection Social Media Big Data
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