Abdaljaleel Maram, Barakat Muna, Mahafzah Azmi, Hallit Rabih, Hallit Souheil, Sallam Malik
Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan.
Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, Jordan.
Narra J. 2024 Aug;4(2):e877. doi: 10.52225/narra.v4i2.877. Epub 2024 Jul 26.
Social media platforms, including TikTok, have become influential sources of health information. However, they also present as potential sources for the spread of vaccine misinformation. The aim of this study was to assess the quality of measles-rubella (MR) vaccine-related contents on TikTok in Jordan and to analyze factors associated with vaccine misinformation. A systematic search for MR vaccine-related TikTok contents in Jordan was conducted using pre-defined keywords and a specified time range. Content metrics (likes, comments, shares, and saves) were collected while the content quality of health information was evaluated using a modified version of the DISCERN, a validated instrument by two expert raters. The average modified DISCERN score ranged from 1, denoting poor content, to 5, indicating excellent content. A total of 50 videos from 34 unique content creators formed the final study sample. The majority of MR vaccine-related content was created by lay individuals (61.8%), followed by TV/news websites/journalists (23.5%), and healthcare professionals (HCPs) (14.7%). The Cohen κ per modified DISCERN item was in the range of 0.579-0.808, <0.001), indicating good to excellent agreement. The overall average modified DISCERN score was 2±1.2, while it was only 1.3±0.52 for lay individuals' content, which indicated poor content quality. For the normalized per number of followers for each source, content by lay individuals had a significantly higher number of likes, saves, and shares with =0.009, 0.012, and 0.004, respectively. Vaccine misinformation was detected in 58.8% of the videos as follows: lay individuals (85.7%), TV/news websites/journalists (25.0%), and HCPs content had none (<0.001). Normalized per the number of followers for each source, videos flagged as having MR vaccine misinformation reached a higher number of likes, saves, and shares (=0.012, 0.016, and 0.003, respectively). In conclusion, substantial dissemination of TikTok MR vaccine-related misinformation in Jordan was detected. Rigorous fact-checking is warranted by the platform to address misinformation on TikTok, which is vital to improve trust in MR vaccination and ultimately protect public health.
包括TikTok在内的社交媒体平台已成为健康信息的重要来源。然而，它们也可能成为疫苗错误信息传播的源头。本研究的目的是评估约旦TikTok上与麻疹风疹（MR）疫苗相关内容的质量，并分析与疫苗错误信息相关的因素。使用预定义的关键词和特定的时间范围，对约旦TikTok上与MR疫苗相关的内容进行了系统搜索。收集了内容指标（点赞、评论、分享和保存），同时由两名专家评分员使用经过修改的DISCERN（一种经过验证的工具）对健康信息的内容质量进行评估。修改后的DISCERN评分平均范围为1（表示内容差）至5（表示内容优秀）。来自34位独立内容创作者的50个视频构成了最终的研究样本。大多数与MR疫苗相关的内容是由非专业人士创作的（61.8%），其次是电视/新闻网站/记者（23.5%）和医疗保健专业人员（HCPs）（14.7%）。每个修改后的DISCERN项目的Cohen κ系数在0.579 - 0.808范围内（<0.001），表明一致性良好到优秀。修改后的DISCERN总体平均评分为2±1.2，而非专业人士的内容平均评分为1.3±0.52，表明内容质量较差。按每个来源的关注者数量进行归一化后，非专业人士创作的内容获得的点赞、保存和分享数量显著更高（分别为 =0.009、0.012和0.004）。在58.8%的视频中检测到疫苗错误信息，情况如下：非专业人士（85.7%）、电视/新闻网站/记者（25.0%），HCPs创作的内容未检测到错误信息（<0.001）。按每个来源的关注者数量进行归一化后，被标记为存在MR疫苗错误信息的视频获得的点赞、保存和分享数量更高（分别为 =0.012、0.016和0.003）。总之，在约旦检测到TikTok上与MR疫苗相关的错误信息大量传播。该平台有必要进行严格的事实核查，以解决TikTok上的错误信息，这对于提高对MR疫苗接种的信任并最终保护公众健康至关重要。
