dc.creator | Gabarron, Elia | es |
dc.creator | Dorronzoro Zubiete, Enrique | es |
dc.creator | Rivera Romero, Octavio | es |
dc.creator | Wynn, Rolf | es |
dc.date.accessioned | 2021-03-02T11:06:39Z | |
dc.date.available | 2021-03-02T11:06:39Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Gabarron, E., Dorronzoro Zubiete, E., Rivera-Romero, O. y Wynn, R. (2019). Diabetes on Twitter: A Sentiment Analysis. Journal of Diabetes Science and Technology, 13 (3), 439-444. | |
dc.identifier.issn | 1932-2968 | es |
dc.identifier.uri | https://hdl.handle.net/11441/105552 | |
dc.description.abstract | 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. | es |
dc.description.sponsorship | Northern Norway Regional Health Authority (Helse Nord RHF), grant HNF1370-17 | es |
dc.format | application/pdf | es |
dc.format.extent | 6 | es |
dc.language.iso | eng | es |
dc.publisher | SAGE Publishing | es |
dc.relation.ispartof | Journal of Diabetes Science and Technology, 13 (3), 439-444. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Diabetes | es |
dc.subject | Sentiment analysis | es |
dc.subject | Social Media | es |
dc.subject | Twitter | es |
dc.subject | Type 1 diabetes | es |
dc.subject | Type 2 diabetes | es |
dc.title | Diabetes on Twitter: A Sentiment Analysis | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.projectID | HNF1370-17 | es |
dc.relation.publisherversion | https://journals.sagepub.com/doi/full/10.1177/1932296818811679 | es |
dc.identifier.doi | 10.1177/1932296818811679 | es |
dc.journaltitle | Journal of Diabetes Science and Technology | es |
dc.publication.volumen | 13 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 439 | es |
dc.publication.endPage | 444 | es |
dc.identifier.sisius | 21591584 | es |
dc.contributor.funder | Northern Norway Regional Health Authority (Helse Nord RHF) | es |