Tweetluenza: Predicting Flu Trends from Twitter Data
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, ca...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020012 |
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| author | Balsam Alkouz Zaher Al Aghbari Jemal Hussien Abawajy |
| author_facet | Balsam Alkouz Zaher Al Aghbari Jemal Hussien Abawajy |
| author_sort | Balsam Alkouz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e4e70d18c2f2438bb817b7717b2a21c5 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-e4e70d18c2f2438bb817b7717b2a21c52025-02-02T06:50:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-12-012427328710.26599/BDMA.2019.9020012Tweetluenza: Predicting Flu Trends from Twitter DataBalsam Alkouz0Zaher Al Aghbari1Jemal Hussien Abawajy2<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Sharjah</institution>, <city>Sharjah</city> <postal-code>27272</postal-code>, <country>UAE</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Sharjah</institution>, <city>Sharjah</city> <postal-code>27272</postal-code>, <country>UAE</country>.<institution content-type="dept">Department of Science, Engineering and Built Environment</institution>, <institution>Deakin University</institution>, <city>Melbourne</city> <postal-code>3125</postal-code>, <country>Australia</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2019.9020012twitter data analysisinfluenza forecastingprediction using social mediasocial media mining |
| spellingShingle | Balsam Alkouz Zaher Al Aghbari Jemal Hussien Abawajy Tweetluenza: Predicting Flu Trends from Twitter Data Big Data Mining and Analytics twitter data analysis influenza forecasting prediction using social media social media mining |
| title | Tweetluenza: Predicting Flu Trends from Twitter Data |
| title_full | Tweetluenza: Predicting Flu Trends from Twitter Data |
| title_fullStr | Tweetluenza: Predicting Flu Trends from Twitter Data |
| title_full_unstemmed | Tweetluenza: Predicting Flu Trends from Twitter Data |
| title_short | Tweetluenza: Predicting Flu Trends from Twitter Data |
| title_sort | tweetluenza predicting flu trends from twitter data |
| topic | twitter data analysis influenza forecasting prediction using social media social media mining |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020012 |
| work_keys_str_mv | AT balsamalkouz tweetluenzapredictingflutrendsfromtwitterdata AT zaheralaghbari tweetluenzapredictingflutrendsfromtwitterdata AT jemalhussienabawajy tweetluenzapredictingflutrendsfromtwitterdata |