Research Letter
doi:10.2196/29723
Keywords
Skin cancer is a growing burden in Canada and the United States. One effective prevention method is the use of sunscreen; however, low sunscreen use [
] coupled with the spread of misinformation online can hinder health promotion activities.Health-related social media posts (including sunscreen) may shape risk-related behaviors of users, so it is important to understand the accuracy of such posts [
].Twitter’s Application Program Interface was used to search for tweets in English containing the word “sunscreen” posted in Canada and the United States (May 1 to August 31, 2019). We used thematic content analysis to elicit the accuracy, sentiment, and theme of the tweets.
Tweets containing verifiable information (that could be assessed as factual or not) were analyzed for accuracy and coded as either “accurate” or “inaccurate” based on current evidence. All tweets were coded for sentiment (positive or negative).
Themes were analyzed using an a priori list of codes based on our previous study [
] and inductively modified based on emergent themes. Differences were tested using the chi-square statistic or the Fisher exact test.In total, 9176 tweets were collected; 167 retweets and 85 irrelevant tweets were excluded. The remaining 8924 tweets were analyzed for accuracy (where applicable), sentiment, and theme. The observed percentage agreement between the coders for sentiment and accuracy was 76%. Only 395 tweets (4% of the total) contained verifiable information and were analyzed for accuracy. Among these, 277 (70%) were accurate and 118 (30%) were inaccurate (
).The most common themes were personal story (n=5425, 61%), tips and recommendations (n=2591, 28%), and advertisements (n=457, 5%). The top theme for accurate and inaccurate tweets was tips and recommendations (n=171, 56%) and personal story (n=90, 62%), respectively.
The sentiment analysis found that 7460 (84%) of tweets had a positive sentiment, 1031 (11%) were mixed or neutral, and 433 (5%) were negative. Among the accurate tweets, the majority had a positive sentiment toward sunscreen (n=248, 89%), while over half (n=64, 54%) of the inaccurate tweets had a negative sentiment. Interestingly, inaccurate tweets were more likely to have any engagement than accurate tweets (
).We found that most tweets were personal stories and not verifiable for accuracy. This suggests that misinformation about sunscreen may not be an important contributor to low sunscreen use, as also noted by Silva et al [
]. The sentiment analysis found that over 80% of all sunscreen tweets had a positive sentiment toward sunscreen use, which is similar to our previous study on sunscreen information in traditional media sources [ ].This study was limited to Twitter; further research on sunscreen misinformation using other social media platforms is recommended.
In conclusion, sunscreen misinformation was limited, but misinformation was more likely to have engagement from users. Organizations may have better success in promoting sunscreen use by producing tailored, engaging sunscreen and cancer prevention messages [
]. Furthermore, it may be beneficial for physicians and health organizations to share messages using lived experience, which may increase reach and engagement online.Category and subcategory | Verifiable tweets | P value (accurate vs inaccurate tweets) | Unverifiable tweets, n (%) | P value (all verifiable vs unverifiable tweets) | ||
Accurate tweets, n (%) | Inaccurate tweets, n (%) | |||||
Sentiment | <.001 | <.001 | ||||
Positive | 248 (89) | 29 (25) | 7183 (84) | |||
Mixed | 19 (7) | 25 (21) | 987 (12) | |||
Negative | 10 (4) | 64 (54) | 359 (4) | |||
Engagementa | .04 | .68 | ||||
0 | 96 (35) | 29 (25) | 2689 (32) | |||
1-5 | 126 (46) | 66 (56) | 4269 (50) | |||
6-10 | 18 (18) | 13 (11) | 711 (8) | |||
>10 | 37 (13) | 10 (8) | 860 (10) | |||
Followersb | .049 | .61 | ||||
0-200 | 73 (26) | 28 (24) | 2011 (24) | |||
201-500 | 55 (20) | 38 (32) | 2222 (26) | |||
501-1000 | 58 (21) | 24 (20) | 1675 (20) | |||
>1000 | 91 (33) | 28 (24) | 2621 (31) | |||
Attached URL | <.001 | <.001 | ||||
Yes | 205 (74) | 60 (51) | 4349 (51) | |||
No | 72 (26) | 50 (49) | 4180 (49) | |||
Type of URL | .30 | <.001 | ||||
Social media | 199 (88) | 50 (83) | 4214 (97) | |||
News | 5 (2) | 0 (0) | 88 (2) | |||
Health organizations | 4 (2) | 1 (2) | 18 (0.4) | |||
Peer-reviewed journal websites | 1 (0.5) | 1 (2) | 2 (0.05) | |||
Other | 17 (8) | 8 (13) | 268 (6) | |||
Attached media | .02 | .86 | ||||
Yes | 57 (21) | 13 (11) | 1482 (17) | |||
No | 220 (79) | 105 (89) | 7047 (83) | |||
Type of media | .87 | .26 | ||||
Photo | 46 (81) | 11 (85) | 1095 (74) | |||
Video | 1 (2) | 0 (0) | 82 (5) | |||
Animated GIF | 10 (17) | 2 (15) | 305 (21) |
aEngagement was defined as the total number of “likes,” “retweets,” “quote tweets,” and “replies” for each tweet.
bFollowers was defined as the number of individual Twitter accounts following the user.
Acknowledgments
This research received no external funding.
Conflicts of Interest
None declared.
References
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Edited by R Dellavalle, MD, PhD, MSPH, T Sivesind; submitted 18.04.21; peer-reviewed by J Makin; comments to author 03.05.21; revised version received 15.06.21; accepted 16.06.21; published 19.07.21
Copyright©Sajjad S Fazel, Emma K Quinn, Chelsea A Ford-Sahibzada, Steven Szarka, Cheryl E Peters. Originally published in JMIR Dermatology (http://derma.jmir.org), 19.07.2021.
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