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International Conference on Health Information Science

HIS 2018: Health Information Science pp 134–145Cite as

Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

Abstract

Social media platforms such as Twitter contain wealth of user-generated data and over time has become a virtual treasure trove of information for knowledge discovery with applications in healthcare, politics, social initiatives, to name a few. Despite the evident benefits of tweets exploration, there are numerous challenges associated with processing such data, given tweets specific characteristics. The study provides a brief of steps involved in manipulation Twitter data as well as offers the examples of the machine learning algorithms most commonly used in text analysis. It concludes with the case study on the Australian hay fever prediction with the application of the selected techniques described in the brief. It demonstrates an example of Twitter real-time analytics for heath condition surveillance with the use of interactive visualisations to assist knowledge discovery and findings dissemination. The results prove the potential of social media to play an important role in meaningful results extraction and guidance for decision makers.

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Author information

Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Sudha Subramani, Sandra Michalska, Hua Wang & Frank Whittaker

  2. Nexus Online Pty Ltd., Greenock, Australia

    Benjamin Heyward

Authors
  1. Sudha Subramani
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  2. Sandra Michalska
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  3. Hua Wang
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  4. Frank Whittaker
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  5. Benjamin Heyward
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Corresponding author

Correspondence to Sudha Subramani .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. James Cook University, Cairns, QLD, Australia

    Ickjai Lee

  3. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Footscray, VIC, Australia

    Hua Wang

  6. James Cook University, Cairns, QLD, Australia

    Wei Xiang

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Subramani, S., Michalska, S., Wang, H., Whittaker, F., Heyward, B. (2018). Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-01078-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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