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Prediction of Influenza-like Illness from Twitter Data and Its Comparison with Integrated Disease Surveillance Program Data

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Computer Networks, Big Data and IoT

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 66))

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Abstract

The social networking sites are currently assisting in delivering faster communication and they are also very useful to know about the different people’s opinions, views, and their sentiments. Twitter is one of the social networking sites, which can help to predict many health-related problems. In this work, sentiment analysis has been performed on tweets to predict the possible number of cases with H1N1 disease. The data will be collected country wise, where the tweets lie between four ranges on which the further analysis will be done. The results show the position of India based on the frequency of occurrence in the tweets as compared to the other countries. This type of disease prediction can help to take a quick decision in order to overcome the damage. The results predicted by sentiment analysis of Twitter data will then compared with the data obtained from the ‘Ministry of Health and Family Welfare-Government of India’ site. The data present at this site gives the actual number of cases occurred and collected by Indian Governments “Integrated Disease Survellience Program”. Comparison with this data will help in calculating the accuracy of the sentiment analysis approach proposed in this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India

    Monica Malik & Sameena Naaz

Authors
  1. Monica Malik
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  2. Sameena Naaz
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Correspondence to Sameena Naaz .

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Editors and Affiliations

  1. Department of CSE, KGiSL Institute of Technology, Coimbatore, India

    A.Pasumpon Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

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Malik, M., Naaz, S. (2021). Prediction of Influenza-like Illness from Twitter Data and Its Comparison with Integrated Disease Surveillance Program Data. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_31

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  • DOI: https://doi.org/10.1007/978-981-16-0965-7_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0964-0

  • Online ISBN: 978-981-16-0965-7

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