research-article

Less is More: With a 280-character limit, Twitter Provides a Valuable Source for Detecting Self-reported Flu Cases

Authors:

Sultanah M. Alshammari,

Rodney D. NielsenAuthors Info & Claims

ICCBD '18: Proceedings of the 2018 International Conference on Computing and Big Data

Pages 1 - 6

https://doi.org/10.1145/3277104.3277105

Published: 08 September 2018 Publication History

Abstract

People in social media post massive amounts of different types of data including text messages, photos, and links. They share their personal opinions, feelings, and even their health status. The high volume of health-related tweets can be used as a tool to track the activities of different infectious diseases. In this paper, we describe our work to process Twitter data to detect self-reported cases of the flu using supervised machine learning methods. The results obtained on a large set of tweets posted in English during the winter season prove that machine learning classifiers are effective in detecting possible self-reported flu cases.

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Cited By

Shah A(2024)Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future DirectionsIEEE Access10.1109/ACCESS.2024.348792312(161820-161841)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3487923
Aliyeva ÇYağanoğlu M(2024)Deep learning approach to detect cyberbullying on twitterMultimedia Tools and Applications10.1007/s11042-024-19869-3Online publication date: 23-Jul-2024
https://doi.org/10.1007/s11042-024-19869-3
Thakur N(2023)Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment AnalysisApplied System Innovation10.3390/asi60500926:5(92)Online publication date: 12-Oct-2023
https://doi.org/10.3390/asi6050092
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Index Terms

Less is More: With a 280-character limit, Twitter Provides a Valuable Source for Detecting Self-reported Flu Cases
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

ICCBD '18: Proceedings of the 2018 International Conference on Computing and Big Data

September 2018

103 pages

ISBN:9781450365406

DOI:10.1145/3277104

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2018

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ICCBD '18: 2018 International Conference on Computing and Big Data

September 8 - 10, 2018

SC, Charleston, USA

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Cited By

Shah A(2024)Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future DirectionsIEEE Access10.1109/ACCESS.2024.348792312(161820-161841)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3487923
Aliyeva ÇYağanoğlu M(2024)Deep learning approach to detect cyberbullying on twitterMultimedia Tools and Applications10.1007/s11042-024-19869-3Online publication date: 23-Jul-2024
https://doi.org/10.1007/s11042-024-19869-3
Thakur N(2023)Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment AnalysisApplied System Innovation10.3390/asi60500926:5(92)Online publication date: 12-Oct-2023
https://doi.org/10.3390/asi6050092
Wu DShead HRen YRaynor PTao YVillanueva HHung PLi XBrookshire REichelberger KGuille CLitwin AOlatosi B(2023)Uncovering the Complexity of Perinatal Polysubstance Use Patterns on X: A Mixed Methods Approach (Preprint)Journal of Medical Internet Research10.2196/53171Online publication date: 28-Sep-2023
https://doi.org/10.2196/53171
Unnikrishnan RS. SV.S. A(2023)Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical dataEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106214123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106214
Showail A(2022)Solving Hajj and Umrah Challenges Using Information and Communication Technology: A SurveyIEEE Access10.1109/ACCESS.2022.319085310(75404-75427)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3190853
Krishnan GSowmya Kamath SSugumaran V(2021)Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary OptimizationNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_23(255-263)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-3-030-80599-9_23
Saadah S(2020)Kemampuan New Media Literacy Remaja dalam Mengenali Cyber Sexual Harassment di SurabayaPalimpsest: Jurnal Ilmu Informasi dan Perpustakaan10.20473/pjil.v11i2.2419711:2(69)Online publication date: 30-Dec-2020
https://doi.org/10.20473/pjil.v11i2.24197

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