research-article

How people talk about health?: Detecting Health Topics from Twitter Streams

Authors:

Carmela Comito,

Nicola ProcopioAuthors Info & Claims

BDIOT '17: Proceedings of the International Conference on Big Data and Internet of Thing

Pages 85 - 90

https://doi.org/10.1145/3175684.3175699

Published: 20 December 2017 Publication History

Abstract

The paper proposes an online clustering algorithm for detecting health-related topics. The method extracts from the tweets relevant terms and incrementally groups them by taking into account both term occurrences and tweet age. A detailed experimentation on the tweets posted by users in US shows that the method is capable to group tweets addressing common health issues into the pertinent topic, outperforming traditional topic model approaches, like Doc-p and LDA.

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Cited By

Shaw GZimmerman MVasquez-Huot LKarami A(2022)Deciphering Latent Health Information in Social Media Using a Mixed-Methods DesignHealthcare10.3390/healthcare1011232010:11(2320)Online publication date: 19-Nov-2022
https://doi.org/10.3390/healthcare10112320
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
Comito CForestiero APizzuti C(2019)Word Embedding based Clustering to Detect Topics in Social MediaIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352518(192-199)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352518

Index Terms

How people talk about health?: Detecting Health Topics from Twitter Streams
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining

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cover image ACM Other conferences

BDIOT '17: Proceedings of the International Conference on Big Data and Internet of Thing

December 2017

251 pages

ISBN:9781450354301

DOI:10.1145/3175684

Copyright © 2017 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: 20 December 2017

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BDIOT2017

BDIOT2017: International Conference on Big Data and Internet of Thing

December 20 - 22, 2017

London, United Kingdom

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

Shaw GZimmerman MVasquez-Huot LKarami A(2022)Deciphering Latent Health Information in Social Media Using a Mixed-Methods DesignHealthcare10.3390/healthcare1011232010:11(2320)Online publication date: 19-Nov-2022
https://doi.org/10.3390/healthcare10112320
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
Comito CForestiero APizzuti C(2019)Word Embedding based Clustering to Detect Topics in Social MediaIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352518(192-199)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352518

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