%0期刊文章%@ 1438- 8871% I JMIR出版物%V 24卡塔尔世界杯8强波胆分析% N 8% P e36085% T评估社交机器人在COVID-19大流行期间的作用:信息、分歧和批评%A Suarez-Lledo,Victor %A Alvarez-Galvez,Javier %+加的斯大学生物医学、生物技术和公共卫生系，Av Ana de Viya, Cádiz, 11009，西班牙，34 956019080，victor.sanz@uca.es %K信息%K社交媒体%K错误信息%K流行病%K疫情%K COVID-19 %K信息病学%K健康促进%K流行病%K聊天机器人%K社交媒体机器人%K推特流%K底测量%K同行支持%D 2022 %7 25.8.2022 %9原始论文%J J医学互联网Res %G英语%X背景:社交媒体改变了我们的生活和交流方式，也为改善我们生活的许多方面提供了前所未有的机会，包括健康促进和疾病预防。然而，社交媒体也有其阴暗的一面，并不总是像它可能的好处那么明显。事实上，社交媒体也为与健康错误信息有关的新的社会和健康风险打开了大门。目的:本研究旨在研究社交媒体机器人在COVID-19爆发期间的作用。方法:使用Twitter流API收集疫情爆发早期关于COVID-19的推文。然后使用底部测量工具来获取每个账户是否是机器人的可能性。机器人分类和主题建模技术用于解释Twitter对话。 Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. Results: Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. Conclusions: By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media. %M 35839385 %R 10.2196/36085 %U //www.mybigtv.com/2022/8/e36085 %U https://doi.org/10.2196/36085 %U http://www.ncbi.nlm.nih.gov/pubmed/35839385