Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector (GloVe) embedding to use the data in tweets for the automatic and efficient identification and classification of dengue. The proposed method classifies tweets related to the outbreak of dengue into positives and negatives. Experiments were conducted to assess the proposed ANN model based on performance evaluation matrices (confusion matrices). The results show that the GloVe vectors can efficiently capture a sufficient amount of information for the classifier to accurately identify and classify tweets as relevant or irrelevant to dengue outbreaks. The proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.