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.