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Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People
In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and bett...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931937/ https://www.ncbi.nlm.nih.gov/pubmed/33693341 http://dx.doi.org/10.3389/fdata.2019.00018 |
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author | Toshniwal, Durga Somani, Soumya Aggarwal, Rohit Malik, Preeti |
author_facet | Toshniwal, Durga Somani, Soumya Aggarwal, Rohit Malik, Preeti |
author_sort | Toshniwal, Durga |
collection | PubMed |
description | In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and better quality of life. Early detection and screening can help doctors nip diseases in their natal stages. However, not everyone is aware of them, which makes it a global issue. The study of the degree of disease awareness amongst people belonging to different nations and continents is a matter of great interest. One method that is suitable for this purpose is using clinical data. But, this data is not readily available. However, today a plethora of platforms are available to people to share their thoughts and experiences. People post about many of the important events in their lives on social media. Their posts offer a microscopic view into their lives and thought processes. Based on this intuition, twitter data pertaining to various chronic and acute diseases has been collected. Tweets for 30 deadly ailments have been collected over a period of 3 months amounting to a total of 19 million. A feature extraction approach is proposed which is used to identify the disease awareness levels across different nations. Deriving the global awareness landscape for ailments can help to identify regions which are well aware and also those that need to get aware. Clustering has been used for this purpose. |
format | Online Article Text |
id | pubmed-7931937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79319372021-03-09 Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People Toshniwal, Durga Somani, Soumya Aggarwal, Rohit Malik, Preeti Front Big Data Big Data In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and better quality of life. Early detection and screening can help doctors nip diseases in their natal stages. However, not everyone is aware of them, which makes it a global issue. The study of the degree of disease awareness amongst people belonging to different nations and continents is a matter of great interest. One method that is suitable for this purpose is using clinical data. But, this data is not readily available. However, today a plethora of platforms are available to people to share their thoughts and experiences. People post about many of the important events in their lives on social media. Their posts offer a microscopic view into their lives and thought processes. Based on this intuition, twitter data pertaining to various chronic and acute diseases has been collected. Tweets for 30 deadly ailments have been collected over a period of 3 months amounting to a total of 19 million. A feature extraction approach is proposed which is used to identify the disease awareness levels across different nations. Deriving the global awareness landscape for ailments can help to identify regions which are well aware and also those that need to get aware. Clustering has been used for this purpose. Frontiers Media S.A. 2019-08-08 /pmc/articles/PMC7931937/ /pubmed/33693341 http://dx.doi.org/10.3389/fdata.2019.00018 Text en Copyright © 2019 Toshniwal, Somani, Aggarwal and Malik. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Toshniwal, Durga Somani, Soumya Aggarwal, Rohit Malik, Preeti Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title | Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title_full | Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title_fullStr | Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title_full_unstemmed | Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title_short | Global Awareness Landscape for Ailments—A Twitter Based Microscopic View Into Thought Processes of People |
title_sort | global awareness landscape for ailments—a twitter based microscopic view into thought processes of people |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931937/ https://www.ncbi.nlm.nih.gov/pubmed/33693341 http://dx.doi.org/10.3389/fdata.2019.00018 |
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