Analysis of 12-lead electrocardiograms shared on Twitter

Abstract

Introduction: A large number of electrocardiograms (ECG) are shared on Twitter every day. Some of them aim to provide information to the readers, and some of them aim to provide training with a mini quiz. This study aimed to discuss the evaluability of ECG images shared on Twitter. Methods: The study sample consisted of 12-lead ECG images shared on Twitter. ECG images shared on 08/01/2020 - 01/31/2021 were manually scanned. Results: A total of 286 tweets matching the criteria were included in the study on the specified dates. The majority of them (n = 231. 80.5%) asked the reader about the ECG. The average number of the tweets' interactions was 70.42 ± 112.17, and the interaction was mainly in the form of "likes" (50.49 ± 80.64). 83.5% of ECGs had a rhythm strip. Total interaction numbers and other parameters were compared. ECGs from which small squares could be selected collected more interactions (p = 0.015). ECGs explained the case or whose diagnosis was clearly stated collected more interactions (p lt;0.001). Also, it was observed that ECGs without a rhythm strip contained more interaction (p lt;0.001). Conclusions: We concluded that 12-derivation ECGs shared on Twitter are highly evaluable. There was also a moderate correlation between the number of followers and the number of interactions. For this reason, it is important for accounts with a high number of followers to following that are experts in their field to prevent information pollution.

Keywords

• sosyal medya
• twitter mesajlaşma
• elektrokardiyografi
• electrocardiography
• social media
• twitter messaging