No abstract

No abstract

The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak. NGOs, Government agencies leverage twitter's open and public features to provide immediate relief. Nevertheless, situational information gets immersed in millions of tweets with varying characteristics. Examining each tweet can be cumbersome and time-consuming. Thus, the efficient extraction of disaster-related tweets and getting information from all the extracted tweets is required. In the current paper, we have developed a novel framework that uses a deep learning-based classification model to separate the situational tweets from others and summarize them in real-time. Our system is a three-phase process: (a) Creating tweet clusters using a representative set of tweets from the initial set of extracted tweets using a multi-objective optimization concept; (b) When a new tweet arrives, the clusters are updated. The new tweet is classified as situational vs. non-situational. If situational, it is assigned to the closest cluster or new cluster. This assignment is based on its weighted average of syntactic and semantic distances and relevancy to the cluster; (c) Summary is formulated by extracting tweets from each cluster. The proposed approach's superior performance on four datasets related to different disaster-related events indicates the developed framework's efficiency over state-of-the-art techniques.

No abstract

No abstract