Purpose: The purpose of this study is to analyze posts shared on Instagram, Twitter, and Reddit referencing scoliosis surgery to evaluate content, tone, and perspective.
Methods: Public posts from Instagram, Twitter, and Reddit were parsed in 2020-2021 and selected based on inclusion of the words 'scoliosis surgery' or '#scoliosissurgery. 100 Reddit posts, 5022 Instagram posts, and 1414 tweets were included in analysis. The Natural Language Toolkit (NLTK) python library was utilized to perform computational text analysis to determine content and sentiment analysis to estimate the tone of posts across each platform.
Results: 46.4% of Tweets were positive in tone, 39.4% were negative, and 13.8% were neutral. Positive content focused on patients, friends, or hospitals sharing good outcomes after a patient's surgery. Negative content focused on long wait times to receive scoliosis surgery. 64.7% of Instagram posts were positive in tone, 16.3% were negative, and 19.0% were neutral. Positive content centered around post-operative progress reports and educational resources, while negative content focused on long-term back pain. 37% of Reddit posts were positive in tone, 38% were negative, and 25% were neutral. Positive posts were about personal post-operative progress reports, while negative posts were about fears prior to scoliosis surgery and questions about risks of the procedure.
Conclusion: This study highlights scoliosis surgery content in social media formats and stratifies how this content is portrayed based on the platform it is on. Surgeons can use this knowledge to better educate and connect with their own patients, thus harnessing the power and reach of social media.
Level of evidence: IV.
Keywords: Computational text analysis; Natural language processing; Scoliosis surgery; Social media.
© 2021. Scoliosis Research Society.