research-article

Identifying Multi-dimensional Information from Microblogs During Epidemics

Authors:

Shalmoli Ghosh,

Saptarshi Ghosh,

Neville DubashAuthors Info & Claims

CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

Pages 224 - 230

https://doi.org/10.1145/3297001.3297030

Published: 03 January 2019 Publication History

Abstract

Microblogging sites like Twitter and Weibo have been shown to provide important real-time information during epidemics and disease outbreaks. During such situations, different types of stakeholders look for different types of information, such as symptoms, prevention, treatment schemes, death reports, and many more. Additionally, lots of personal opinions, sentiments are also posted on social media along with factual contents. In this work, we propose a method to automatically classify tweets posted during an epidemic into various informative categories. To this end, we utilize features derived from a medical knowledge base (UMLS) as well as features based on syntactic and lexical structure of tweets. We apply the classifier over tweets related to several diseases (Ebola, Dengue, and MERS), and show that, the proposed approach yields better classification performance as compared to earlier works. We also identify some interesting directions of future work, e.g., applying the classifier over drug addictions like Opioid.

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Cited By

Asaad CKhaouja IGhogho MBaïna K(2025)When Infodemic Meets Epidemic: Systematic Literature ReviewJMIR Public Health and Surveillance10.2196/5564211(e55642)Online publication date: 3-Feb-2025
https://doi.org/10.2196/55642
Nguyen TFisichella MRudra K(2024)A Trustworthy Approach to Classify and Analyze Epidemic-Related Information From MicroblogsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339139511:5(6229-6241)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3391395
Priya SJoshi VChandra J(2024)Let’s explain crisis: deep multi-scale hierarchical attention framework for crisis-task identificationThe Journal of Supercomputing10.1007/s11227-024-06150-580:12(17923-17951)Online publication date: 6-May-2024
https://doi.org/10.1007/s11227-024-06150-5
Show More Cited By

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cover image ACM Other conferences

CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

January 2019

380 pages

ISBN:9781450362078

DOI:10.1145/3297001

General Chairs:
Lipika Dey
TCS Innovation Labs
,
Surajit Chaudhury
Microsoft Research
,
Program Chairs:
Raghu Krishnapuram
Robert Bosch Center, IISc Bangalore
,
Parag Singla
IIT Delhi
,
Publications Chair:
Rishiraj Saha Roy
Max Planck Institute for Informatics

Copyright © 2019 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 January 2019

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CoDS-COMAD '19

CoDS-COMAD '19: 6th ACM IKDD CoDS and 24th COMAD

January 3 - 5, 2019

Kolkata, India

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CODS-COMAD '19 Paper Acceptance Rate 62 of 198 submissions, 31%;

Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

Asaad CKhaouja IGhogho MBaïna K(2025)When Infodemic Meets Epidemic: Systematic Literature ReviewJMIR Public Health and Surveillance10.2196/5564211(e55642)Online publication date: 3-Feb-2025
https://doi.org/10.2196/55642
Nguyen TFisichella MRudra K(2024)A Trustworthy Approach to Classify and Analyze Epidemic-Related Information From MicroblogsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339139511:5(6229-6241)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3391395
Priya SJoshi VChandra J(2024)Let’s explain crisis: deep multi-scale hierarchical attention framework for crisis-task identificationThe Journal of Supercomputing10.1007/s11227-024-06150-580:12(17923-17951)Online publication date: 6-May-2024
https://doi.org/10.1007/s11227-024-06150-5
Yu XXie ZMashhadi AHong LBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)Multi-task Models for Multi-faceted Classification of Pandemic Information on Social MediaProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531552(327-335)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531552
Agarwal AUniyal DToshniwal DDeb D(2021)Dense Vector Embedding Based Approach to Identify Prominent Disseminators From Twitter Data Amid COVID-19 OutbreakIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2021.30676615:3(308-320)Online publication date: Jun-2021
https://doi.org/10.1109/TETCI.2021.3067661
Shahid FOny SAlbi TChellappan SVashistha AIslam A(2020)Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue EpidemicProceedings of the ACM on Human-Computer Interaction10.1145/33928754:CSCW1(1-27)Online publication date: 29-May-2020
https://dl.acm.org/doi/10.1145/3392875

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