The Community for Technology Leaders
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017)
Atlanta, Georgia, USA
June 5, 2017 to June 8, 2017
ISSN: 1063-6927
ISBN: 978-1-5386-1792-2
pp: 1240-1249
ABSTRACT
We study the problem of using Social Media to detect natural disasters, of which we are interested in a special kind, namely landslides. Employing information from Social Media presents unique research challenges, as there exists a considerable amount of noise due to multiple meanings of the search keywords, such as "landslide" and "mudslide". To tackle these challenges, we propose REX, a rapid ensemble classification system which can filter out noisy information by implementing two key ideas: (I) a new method for constructing independent classifiers that can be used for rapid ensemble classification of Social Media texts, where each classifier is built using randomized Explicit Semantic Analysis; and (II) a self-correction approach which takes advantage of the observation that the majority label assigned to Social Media texts belonging to a large event is highly accurate. We perform experiments using real data from Twitter over 1.5 years to show that REX classification achieves 0.98 in F-measure, which outperforms the standard Bag-of-Words algorithm by an average of 0.14 and the state-of-the-art Word2Vec algorithm by 0.04. We also release the annotated datasets used in the experiments as a contribution to the research community containing 282k labeled items.
INDEX TERMS
Terrain factors, Semantics, Twitter, Encyclopedias, Electronic publishing
CITATION

A. Musaev, D. Wang, J. Xie and C. Pu, "REX: Rapid Ensemble Classification System for Landslide Detection Using Social Media," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, Georgia, USA, 2017, pp. 1240-1249.
doi:10.1109/ICDCS.2017.207
80 ms
(Ver 3.3 (11022016))