@文章{info:doi/ 10.21960 /36085，作者="Suarez-Lledo, Victor和Alvarez-Galvez, Javier"，标题="评估社交机器人在COVID-19大流行期间的作用:信息流行、分歧和批评"，期刊="J Med Internet Res"，年="2022"，月="8"，日="25"，卷="24"，数="8"，页="e36085"，关键词="信息流行;社交媒体;错误信息;流行;爆发;COVID-19;infodemiology;健康促进;大流行;聊天机器人; social media bot; Twitter stream; Botometer; peer support", abstract="Background: Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. Objective: This study aimed to study the role of social media bots during the COVID-19 outbreak. Methods: The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. 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. ", issn="1438-8871", doi="10.2196/36085", url="//www.mybigtv.com/2022/8/e36085", url="https://doi.org/10.2196/36085", url="http://www.ncbi.nlm.nih.gov/pubmed/35839385" }