Year: 2024
Type: article
Abstract: Aims Notoriously described as being controversial in nature, we aim to objectively assess the language used amongst medical professionals in social media (SM). Methods Data was extracted from the Reddit SM website under the sub-page "r/DoctorsUK" forum, through a data-extraction code written in the python language. This was then analysed through a Natural Language Toolkit algorithm to classify language used into positive, negative and neutral sentiments with an overall polarity score ranging from -1 noting most negative to 1 being most positive. Results A total of 992 articles were examined over a 24-day period, as limited by Reddit data application programme interface (API) protocols. A total of 210 positive, 218 negative and 564 neutral articles were noted with an average of 43 articles posted per day. Headlines sentiment on average was overall marginally positive at 0.0154. Comment tree within each headline was also examined, noting a more positive 0.173. Through regression analysis, it was noted that headline and comment sentiments was directly correlated, and this was statistically significant(P=1.24*10-10). It was also noted that more negative headlines had higher “likes” and were therefore featured more prominently within the forum. Conclusions Although SM posts are overall positive in sentiment, it is noted that more negative sentiments are often up-voted and therefore featured more prominently due to their more provocative nature and emotions incited amongst readers. As SM posts elicits strong psychological and psychosocial reactions from a community, there is an urgency to more accurate data driven models in developing future policies to maintain an effective professional morale within the NHS.
Source: British journal of surgery
Author: Kesav Aditya
Institution: University Hospitals Sussex NHS Foundation Trust
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4402375950