A Deep Learning Approach to Outbreak related Tweet Detection

Abstract

Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet detection.