Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

• Main Authors: Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, Muhammad Adnan
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2021/5520366

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.