Gabarron Elia, Dorronzoro Enrique, Rivera-Romero Octavio, Wynn Rolf
1 Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.
2 Department of Electronic Technology, Universidad de Sevilla, Sevilla, Spain.
J Diabetes Sci Technol. 2019 May;13(3):439-444. doi: 10.1177/1932296818811679. Epub 2018 Nov 19.
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.
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.
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).
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.
社交媒体上发布的内容会对个人及其决策产生影响。了解对糖尿病的看法对于理解此类信息可能对患有这种健康状况的人群及其家庭成员产生的影响至关重要。本研究的目的是分析在推特上发布的有关糖尿病的信息中所表达的看法。
使用推特标准应用程序编程接口（API），在一周内提取包含“糖尿病”“1型糖尿病”和/或“2型糖尿病”等术语之一的推文。仅提取文本信息和用户的关注者数量。使用情感强度分析工具（SentiStrength）进行情感分析。
共自动提取了67421条推文，其中3.7%专门提及1型糖尿病，6.8%专门提及2型糖尿病。7.0%的帖子中包含一个或多个表情符号。专门提及2型糖尿病且不包含表情符号的推文比包含表情符号的推文明显更负面（-2.22对-1.48，P <.001）。关于1型糖尿病且包含表情符号的推文比不包含表情符号的推文在积极方面更显著，在消极方面也更不明显（分别为1.71对1.49和-1.31对-1.50；P <.005）。关注者数量与积极情感强度呈负相关（r = -.023，P <.001），与消极情感呈正相关（r =.016，P <.001）。
在社交媒体上使用情感分析技术可以增加我们对社交媒体如何影响糖尿病患者及其家庭的了解，并有助于改进公共卫生策略。
