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...
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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 Type 1 diabetes Type 2 diabetes |
spellingShingle |
Diabetes Sentiment analysis Social Media 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 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 |