A Crisis Information Dashboard System using Feedback-Based Text Classification of Typhoon-Related Tweets in the Philippines

Abstract

In this paper, we contribute to social media analytics literature by incorporating user feedback towards improving Tweet classification of code-switch data. We integrate this technology in a crisis information dashboard system to consolidate significant information. The instantaneous nature of data obtained from social media makes it an ideal medium in emergency situations. Using a multiclass SVM with categories (1) Announcement, (2) Casualty and Damage, and (3) Call for Help, our test case involving typhoon Hagupit with a total of 1690 tweets resulted with an accuracy rate of 63.238% as baseline. In a simulated deployment, 67 mislabeled tweets were corrected by the users, which increased the accuracy by 1%. Future work on this study can include increasing the added instances to observe a more significant difference in metrics, and to compare the difference if only corrected mislabeled tweets were added in each iteration of retraining. Multilabel classification can also be considered.