@inproceedings{ea6e0dd5da3045e3976b5d8467ae1b56,
title = "Distance-based outliers method for detecting disease outbreaks using social media",
abstract = "Forecasting the disease outbreaks could be useful for decision-making of public health resources. Social media provides a low-cost alternative source for public health surveillance. In this research we use Twitter data as a demonstration to detect influenza outbreak. We use distance-based outliers method to transform the noisy Twitter data into regions and then use regions to do region-based hypothesis testing for rapid outbreak detection. Majority voting has been used for decision making in committees. Our simulations show a good accuracy and robustness.",
keywords = "Anomaly Detection, Big data, Distance-based Outliers, Hypothesis Testing, Outbreak Detection, Public Health, Social Network, Surveillance, Time Series Analysis, Twitter",
author = "Xiangfeng Dai and Marwan Bikdash",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; SoutheastCon 2016 ; Conference date: 30-03-2016 Through 03-04-2016",
year = "2016",
month = jul,
day = "7",
doi = "10.1109/SECON.2016.7506752",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "SoutheastCon 2016",
}
