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Health authorities worldwide strive to detect Influenza prevalence as early as possible in order to prepare for it and minimize its impacts. To this end, we address the Influenza prevalence surveillance and prediction problem. In this paper, we develop a new Influenza prevalence prediction model, called Tweetluenza, to predict the spread of the Influenza in real time using cross-lingual data harvested from Twitter data streams with emphases on the United Arab Emirates (UAE). Based on the features of tweets, Tweetluenza filters the Influenza tweets and classifies them into two classes, reporting and non-reporting. To monitor the growth of Influenza, the reporting tweets were employed. Furthermore, a linear regression model leverages the reporting tweets to predict the Influenza-related hospital visits in the future. We evaluated Tweetluenza empirically to study its feasibility and compared the results with the actual hospital visits recorded by the UAE Ministry of Health. The results of our experiments demonstrate the practicality of Tweetluenza, which was verified by the high correlation between the Influenza-related Twitter data and hospital visits due to Influenza. Furthermore, the evaluation of the analysis and prediction of Influenza shows that combining English and Arabic tweets improves the correlation results.


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Tweetluenza: Predicting Flu Trends from Twitter Data

Show Author's information Balsam AlkouzZaher Al Aghbari( )Jemal Hussien Abawajy
Department of Computer Science, University of Sharjah, Sharjah 27272, UAE.
Department of Science, Engineering and Built Environment, Deakin University, Melbourne 3125, Australia.

Abstract

Health authorities worldwide strive to detect Influenza prevalence as early as possible in order to prepare for it and minimize its impacts. To this end, we address the Influenza prevalence surveillance and prediction problem. In this paper, we develop a new Influenza prevalence prediction model, called Tweetluenza, to predict the spread of the Influenza in real time using cross-lingual data harvested from Twitter data streams with emphases on the United Arab Emirates (UAE). Based on the features of tweets, Tweetluenza filters the Influenza tweets and classifies them into two classes, reporting and non-reporting. To monitor the growth of Influenza, the reporting tweets were employed. Furthermore, a linear regression model leverages the reporting tweets to predict the Influenza-related hospital visits in the future. We evaluated Tweetluenza empirically to study its feasibility and compared the results with the actual hospital visits recorded by the UAE Ministry of Health. The results of our experiments demonstrate the practicality of Tweetluenza, which was verified by the high correlation between the Influenza-related Twitter data and hospital visits due to Influenza. Furthermore, the evaluation of the analysis and prediction of Influenza shows that combining English and Arabic tweets improves the correlation results.

Keywords:

Twitter data analysis, Influenza forecasting, prediction using social media, social media mining
Received: 08 January 2019 Revised: 12 May 2019 Accepted: 22 May 2019 Published: 05 August 2019 Issue date: December 2019
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Publication history

Received: 08 January 2019
Revised: 12 May 2019
Accepted: 22 May 2019
Published: 05 August 2019
Issue date: December 2019

Copyright

© The author(s) 2019

Acknowledgements

The authors would like to express their sincere thanks to the UAE Ministry of Health specially their Statistics and Research Center for providing us the actual counts of Influenza-related hospital visits to be used in our research.

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