Elia Gabarron1, Enrique Dorronzoro2, Octavio Rivera-Romero2, Rolf Wynn3,4. 1. 1 Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway. 2. 2 Department of Electronic Technology, Universidad de Sevilla, Sevilla, Spain. 3. 3 Department of Clinical Medicine, Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway. 4. 4 Division of Mental Health and Addictions, University Hospital of North Norway, Tromsø, Norway.
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.
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.
Entities:
Keywords:
Twitter; diabetes; sentiment analysis; social media; type 1 diabetes; type 2 diabetes
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