Comparing rule-based methods and pre-trained language models to classify flood related Tweets

Social media presents a rich source of real-time information provided by individual users in emergency situations. However, due to its unstructured nature and high volume, it is challenging to extract key information from these continuous data streams. This paper compares the ability to identify relevant flood related Tweets between a deep neural classification model known as a transformer, and a simple rule-based classification. Results show that the classification model out-performs the rule-based approach, at the time-cost of labelling and training the model.