Ng Qin Xiang, Wee Liang En, Lim Yu Liang, Ong Rebecca Hui Shan, Ong Clarence, Venkatachalam Indumathi, Liew Tau Ming
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
SingHealth Duke-NUS Global Health Institute, Duke-NUS Medical School, Singapore, Singapore.
Front Public Health. 2024 Dec 16;12:1491087. doi: 10.3389/fpubh.2024.1491087. eCollection 2024.
To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT). This method allowed us to process and analyze large-scale textual data, focusing on individual user tweets. We then employed BERT-based topic modeling, followed by reflexive thematic analysis to categorize and further refine tweets into coherent themes to interpret the overarching narratives within the long COVID discourse. In contrast to prior studies, the constructs framing our analyses were data driven as well as informed by the tenets of social constructivism.
Out of an initial dataset of 2,905,906 tweets, a total of 1,354,889 unique, English-language tweets from individual users were included in the final dataset for analysis. Three main themes were generated: (1) General discussions of long COVID, (2) Skepticism about long COVID, and (3) Adverse effects of long COVID on individuals. These themes highlighted various aspects, including public awareness, community support, misinformation, and personal experiences with long COVID. The analysis also revealed a stable temporal trend in the long COVID discussions from 2020 to 2023, indicating its sustained interest in public discourse.
Social media, specifically X, helped in shaping public awareness and perception of long COVID, and the posts demonstrate a collective effort in community building and information sharing.
通过2020年5月至2023年4月期间X（前身为推特）上的帖子，描述围绕长期新冠的公众对话。
以X为数据源，我们提取了2020年5月至2023年4月期间发布的包含#long - covid、#long_covid或“long covid”的推文。然后，我们使用来自变换器的双向编码器表示（BERT）进行无监督深度学习分析。这种方法使我们能够处理和分析大规模文本数据，重点关注单个用户的推文。然后，我们采用基于BERT的主题建模，随后进行反思性主题分析，将推文分类并进一步提炼为连贯的主题，以解读长期新冠话语中的总体叙事。与先前的研究不同，构建我们分析的结构是由数据驱动的，并受到社会建构主义原则的影响。
在最初的2,905,906条推文数据集中，最终用于分析的数据集包含来自单个用户的总共1,354,889条独特的英语推文。生成了三个主要主题：（1）对长期新冠的一般性讨论，（2）对长期新冠的怀疑，以及（3）长期新冠对个人的不良影响。这些主题突出了各个方面，包括公众意识、社区支持、错误信息以及长期新冠的个人经历。分析还揭示了2020年至2023年期间长期新冠讨论的稳定时间趋势，表明公众对话对其持续关注。
社交媒体，特别是X，有助于塑造公众对长期新冠的认识和看法，这些帖子展示了社区建设和信息共享方面的集体努力。
