Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain.
Computational Social Science DataLab, University Research Institute on Social Sciences, University of Cadiz, Jerez de la Frontera, Spain.
J Med Internet Res. 2022 Aug 25;24(8):e36085. doi: 10.2196/36085.
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.
This study aimed to study the role of social media bots during the COVID-19 outbreak.
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.
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.
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.
社交媒体改变了我们的生活和交流方式,同时也为改善我们生活的许多方面提供了前所未有的机会,包括促进健康和预防疾病。然而,社交媒体也有不那么光明的一面,其潜在风险并不总是那么明显。事实上,社交媒体也为与健康错误信息相关的新的社会和健康风险打开了大门。
本研究旨在研究社交媒体机器人在 COVID-19 爆发期间的作用。
使用 Twitter 流 API 在疫情早期收集有关 COVID-19 的推文。然后使用 Botometer 工具获得每个账户是机器人的可能性。使用机器人分类和主题建模技术来解释 Twitter 对话。最后,根据推文的来源比较与推文相关的情绪。
就对话主题而言,不同账户之间存在明显差异。非机器人账户的内容与大流行的演变、支持和建议有关。另一方面,就自我宣称的机器人而言,内容主要是新闻,例如诊断测试的存在、大流行的演变和科学发现。最后,在机器人的情况下,内容主要是政治方面的。总之,普遍存在批评和分歧的主导基调。关于情绪分析,主要差异与对话的基调有关。就自我宣称的机器人而言,这种情况往往是中性的,而正常用户的对话则呈积极态势。相比之下,机器人往往呈消极态势。
通过根据其成为机器人的可能性对账户进行分类并进行主题建模,我们能够对有关 COVID-19 的 Twitter 对话进行细分。机器人账户倾向于批评为遏制大流行而采取的措施,对政治家表示不满,或对社交媒体上分享的信息的真实性表示质疑。