Diabetes on Twitter: A Sentiment Analysis

Background: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The obj...

Full description

Bibliographic Details
Main Authors: Gabarron, Elia, Dorronzoro Zubiete, Enrique, Rivera Romero, Octavio, Wynn, Rolf
Other Authors: Universidad de Sevilla. Departamento de Tecnología Electrónica, Northern Norway Regional Health Authority (Helse Nord RHF)
Format: Article in Journal/Newspaper
Language:English
Published: SAGE Publishing 2021
Subjects:
Online Access:https://idus.us.es/handle//11441/105552
id ftunivsevillair:oai:idus.us.es:11441/105552
record_format openpolar
spelling ftunivsevillair:oai:idus.us.es:11441/105552 2024-02-11T10:07:08+01:00 Diabetes on Twitter: A Sentiment Analysis Gabarron, Elia Dorronzoro Zubiete, Enrique Rivera Romero, Octavio Wynn, Rolf Universidad de Sevilla. Departamento de Tecnología Electrónica Northern Norway Regional Health Authority (Helse Nord RHF) 2021-03-02T11:06:39Z https://idus.us.es/handle//11441/105552 eng eng SAGE Publishing Journal of Diabetes Science and Technology, 13 (3), 439-444. HNF1370-17 https://journals.sagepub.com/doi/full/10.1177/1932296818811679 https://idus.us.es/handle//11441/105552 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Diabetes Sentiment analysis Social Media Twitter Type 1 diabetes Type 2 diabetes info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2021 ftunivsevillair 2024-01-24T00:27:38Z Background: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter. Method: Tweets including one of the terms “diabetes,” “t1d,” and/or “t2d” were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength. Results: A total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (–2.22 vs −1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and −1.31 vs −1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength (r = –.023, P < .001) and a positive association with negative sentiment (r = .016, P < .001). Conclusion: The use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies. Northern Norway Regional Health Authority (Helse Nord RHF), grant HNF1370-17 Article in Journal/Newspaper Northern Norway idUS - Deposito de Investigación Universidad de Sevilla Norway
institution Open Polar
collection idUS - Deposito de Investigación Universidad de Sevilla
op_collection_id ftunivsevillair
language English
topic Diabetes
Sentiment analysis
Social Media
Twitter
Type 1 diabetes
Type 2 diabetes
spellingShingle Diabetes
Sentiment analysis
Social Media
Twitter
Type 1 diabetes
Type 2 diabetes
Gabarron, Elia
Dorronzoro Zubiete, Enrique
Rivera Romero, Octavio
Wynn, Rolf
Diabetes on Twitter: A Sentiment Analysis
topic_facet Diabetes
Sentiment analysis
Social Media
Twitter
Type 1 diabetes
Type 2 diabetes
description Background: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter. Method: Tweets including one of the terms “diabetes,” “t1d,” and/or “t2d” were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength. Results: A total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (–2.22 vs −1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and −1.31 vs −1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength (r = –.023, P < .001) and a positive association with negative sentiment (r = .016, P < .001). Conclusion: The use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies. Northern Norway Regional Health Authority (Helse Nord RHF), grant HNF1370-17
author2 Universidad de Sevilla. Departamento de Tecnología Electrónica
Northern Norway Regional Health Authority (Helse Nord RHF)
format Article in Journal/Newspaper
author Gabarron, Elia
Dorronzoro Zubiete, Enrique
Rivera Romero, Octavio
Wynn, Rolf
author_facet Gabarron, Elia
Dorronzoro Zubiete, Enrique
Rivera Romero, Octavio
Wynn, Rolf
author_sort Gabarron, Elia
title Diabetes on Twitter: A Sentiment Analysis
title_short Diabetes on Twitter: A Sentiment Analysis
title_full Diabetes on Twitter: A Sentiment Analysis
title_fullStr Diabetes on Twitter: A Sentiment Analysis
title_full_unstemmed Diabetes on Twitter: A Sentiment Analysis
title_sort diabetes on twitter: a sentiment analysis
publisher SAGE Publishing
publishDate 2021
url https://idus.us.es/handle//11441/105552
geographic Norway
geographic_facet Norway
genre Northern Norway
genre_facet Northern Norway
op_relation Journal of Diabetes Science and Technology, 13 (3), 439-444.
HNF1370-17
https://journals.sagepub.com/doi/full/10.1177/1932296818811679
https://idus.us.es/handle//11441/105552
op_rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
_version_ 1790605287552450560