A Comparative Study of Microblogs Features Effectiveness for the Identification of Prominent Microblog Users During Unexpected Disaster

This paper presents a learning-based approach for the selection of relevant feature categories in the context of information retrieval from microblogs during unexpected disasters. Our information retrieval strategy consists of identifying prominent microblog users who are susceptible to share relevant and exclusive information in a disaster case. To identify these users, we evaluate the effectiveness of the state-of-the-art features characterizing microblog users for the identification of prominent users in a specific context. We experimented with a different sets of feature categories to determine those that discriminate prominent users sets from non-prominent ones interacting in Twitter during the 2014 Herault floods that occurred in France. The achieved results show that on- and off-topical user activities features are the most representative features for identifying prominent users in a disaster context. We also note that SVM outperforms the ANN learning algorithm for this classification context especially when it is trained with additional spatial features.