On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
This addresses the challenge of timely disaster response by improving tweet classification for first responders and decision makers, though it appears incremental as it compares existing approaches rather than introducing fundamentally new methods.
The paper tackles the problem of automatically identifying disaster-related tweets for situational awareness, comparing matching-based and learning-based approaches. Their matching-based algorithm using keywords and hashtags achieved higher quality relevant tweets and more interpretable sentiment analysis results across eleven disaster datasets.
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach.