TY - JOUR AU - Portelli, Beatrice AU - Scaboro, Simone AU - Tonino, Roberto AU - Chersoni, Emmanuele AU - Santus, Enrico AU - Serra, Giuseppe PY - 2022 DA - 2022/5/13 TI -监测twitter圈中COVID-19疫苗的用户意见和副作用:Infodemiology研究微博乔- J地中海互联网Res SP - e35115六世- 24 - 5 KW -药物不良事件KW - COVID-19 KW -数字药物警戒KW -意见挖掘KW -疫苗KW -社会媒体KW -机器学习KW -深度学习KW -学习模型KW -情绪分析KW - Twitter分析KW - Twitter KW -门户网站KW -公共卫生AB -背景:在当前阶段的COVID-19大流行,我们正在见证人类历史上最大规模的疫苗分发。像任何其他药物一样，疫苗可能会产生意想不到的副作用，需要及时进行调查，以尽量减少对人群的伤害。如果处理不当，副作用也可能影响公众对国家政府开展的疫苗接种运动的信任。目的:监测社交媒体以早期发现副作用，了解公众对疫苗的意见，对于确保疫苗的成功和无害推广至关重要。本研究的目的是创建一个门户网站，以监测社交媒体用户对COVID-19疫苗的意见，这可以为记者、科学家和用户提供一个工具，以直观地了解公众对疫苗接种运动的反应。方法:我们开发了一个工具来分析Twitter上关于COVID-19疫苗的公众舆论，利用了最先进的社交媒体药物不良事件识别系统等技术;情感分析的自然语言处理模型统计工具;以及开源数据库，将热门话题标签、新闻文章及其真实性可视化。 All modules of the system are displayed through an open web portal. Results: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot–related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions: We present a tool connected with a web portal to monitor and display some key aspects of the public’s reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model. SN - 1438-8871 UR - //www.mybigtv.com/2022/5/e35115 UR - https://doi.org/10.2196/35115 UR - http://www.ncbi.nlm.nih.gov/pubmed/35446781 DO - 10.2196/35115 ID - info:doi/10.2196/35115 ER -