Intelligent Disaster Response via Social Media Analysis A Survey <s> Data Extraction <s> We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classification approach can improve prediction accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the location of Twitter users. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Data Extraction <s> During the course of several natural disasters in recent years, Twitter has been found to play an important role as an additional medium for many-to-many crisis communication. Emergency services are successfully using Twitter to inform the public about current developments, and are increasingly also attempting to source first-hand situational information from Twitter feeds (such as relevant hashtags). The further study of the uses of Twitter during natural disasters relies on the development of flexible and reliable research infrastructure for tracking and analysing Twitter feeds at scale and in close to real time, however. This article outlines two approaches to the development of such infrastructure: one which builds on the readily available open source platform yourTwapperkeeper to provide a low-cost, simple, and basic solution; and one which establishes a more powerful and flexible framework by drawing on highly scaleable, state-of-the-art technology. <s> BIB002
Disaster-related tweets are extracted using lexiconbased [39; 78] or location-based BIB001 methods. The former uses a set of keywords that are generated by experts and the latter collects all the tweets that are associated with a specific location. Tweets that are filtered using keywords are only a fraction of all the disaster-related ones BIB002 and tweets with location information are quite rare. Hence both methods lack completeness and have low coverage.