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Twitter-based Influenza Surveillance: An Analysis of the 2016-2017 and 2017-2018 Seasons in Italy

Authors:

Carmela Comito,

Agostino Forestiero,

Clara PizzutiAuthors Info & Claims

IDEAS '18: Proceedings of the 22nd International Database Engineering & Applications Symposium

Pages 175 - 182

https://doi.org/10.1145/3216122.3216128

Published: 18 June 2018 Publication History

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Abstract

Influenza surveillance through social media data is becoming an important research topic because it could enhance the capabilities of official surveillance systems in monitoring the outbreak of seasonal flu, by providing healthcare organization with improved situational awareness. In this paper, the two influenza seasons 2016-2017 and 2017-2018, restricted to Italy, are investigated by analyzing the tweets posted by users regarding influenza-like illness. Two types of analysis are performed. The first studies the correlation between the tweets containing the most frequent flu related words with the data provided by the Italian InfluNet surveillance system. The second one examines the sentiment of people on the medicines used to heal flu. We show that there is a strict correlation between the reports published on the InfluNet system, and the contents posted by Twitter users about their symptoms and health state. Moreover, we found that the sentiment expressed by people regarding the treatment, in terms of medicines, taken to heal seems rather negative.

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Chang VXu QNg CGuizani MHossain M(2022)How Do People View COVID-19 VaccinesJournal of Global Information Management10.4018/JGIM.30081730:10(1-29)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.4018/JGIM.300817
Kheruwala HAhmad MVerma JTanwar SSingh P(2022)Context-Enriched Machine Learning-Based Approach for Sentiment AnalysisRecent Innovations in Computing10.1007/978-981-16-8892-8_6(67-84)Online publication date: 16-Apr-2022
https://doi.org/10.1007/978-981-16-8892-8_6
KÜÇÜK DARICI NKÜÇÜK E(2021)Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derlemeÖmer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi10.28948/ngumuh.778948Online publication date: 6-Jan-2021
https://doi.org/10.28948/ngumuh.778948
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Index Terms

Twitter-based Influenza Surveillance: An Analysis of the 2016-2017 and 2017-2018 Seasons in Italy
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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Published In

IDEAS '18: Proceedings of the 22nd International Database Engineering & Applications Symposium

June 2018

328 pages

ISBN:9781450365277

DOI:10.1145/3216122

Editor:
Bipin C. Desai
Concordia University
,
General Chairs:
Bipin C. Desai
Concordia University
,
Sergio Flesca
University of Calabria
,
Program Chairs:
Ester Zumpano
University of Calabria
,
Elio Masciari
ICAR-CNR
,
Luciano Caroprese
University of Calabria

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Concordia University: Concordia University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2018

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IDEAS 2018

IDEAS 2018: 22nd International Database Engineering & Applications Symposium

June 18 - 20, 2018

Villa San Giovanni, Italy

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Chang VXu QNg CGuizani MHossain M(2022)How Do People View COVID-19 VaccinesJournal of Global Information Management10.4018/JGIM.30081730:10(1-29)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.4018/JGIM.300817
Kheruwala HAhmad MVerma JTanwar SSingh P(2022)Context-Enriched Machine Learning-Based Approach for Sentiment AnalysisRecent Innovations in Computing10.1007/978-981-16-8892-8_6(67-84)Online publication date: 16-Apr-2022
https://doi.org/10.1007/978-981-16-8892-8_6
KÜÇÜK DARICI NKÜÇÜK E(2021)Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derlemeÖmer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi10.28948/ngumuh.778948Online publication date: 6-Jan-2021
https://doi.org/10.28948/ngumuh.778948
de Melo TFigueiredo C(2021)Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling ApproachJMIR Public Health and Surveillance10.2196/245857:2(e24585)Online publication date: 10-Feb-2021
https://doi.org/10.2196/24585
T.K. BAnnavarapu CBablani A(2021)Machine learning algorithms for social media analysis: A surveyComputer Science Review10.1016/j.cosrev.2021.10039540(100395)Online publication date: May-2021
https://doi.org/10.1016/j.cosrev.2021.100395
Gupta AKatarya R(2020)Social media based surveillance systems for healthcare using machine learning: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2020.103500108(103500)Online publication date: Aug-2020
https://doi.org/10.1016/j.jbi.2020.103500
Comito CForestiero APapuzzo G(2019)Exploiting Social Media to enhance Clinical Decision SupportIEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume10.1145/3358695.3360899(244-249)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3358695.3360899

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Real-time disease surveillance using Twitter data: demonstration on flu and cancer

Mining Twitter data for influenza detection and surveillance

Outpatient physician billing data for age and setting specific syndromic surveillance of influenza-like illnesses

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