A user-generated text on social media enables health workers to keep track of
information, identify possible outbreaks, forecast disease trends, monitor
emergency cases, and ascertain disease awareness and response to official
health correspondence. This exchange of health information on social media has
been regarded as an attempt to enhance public health surveillance (PHS).
Despite its potential, the technology is still in its early stages and is not
ready for widespread application. Advancements in pretrained language models
(PLMs) have facilitated the development of several domain-specific PLMs and a
variety of downstream applications. However, there are no PLMs for social media
tasks involving PHS. We present and release PHS-BERT, a transformer-based PLM,
to identify tasks related to public health surveillance on social media. We
compared and benchmarked the performance of PHS-BERT on 25 datasets from
different social medial platforms related to 7 different PHS tasks. Compared
with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT
achieved state-of-the-art performance on all 25 tested datasets, showing that
our PLM is robust and generalizable in the common PHS tasks. By making PHS-BERT
available, we aim to facilitate the community to reduce the computational cost
and introduce new baselines for future works across various PHS-related tasks.

使用社交媒体上用户生成的文本数据进行公共卫生监测的研究表明，使用预训练语言模型（PLM）的进展促进了针对社交媒体上公共卫生监测任务的新模型的开发。本文介绍了一种名为 PHS-BERT 的 PLM，该模型在公共卫生监测任务上的表现优于现有的 PLMs，并在广泛的数据集上表现出鲁棒性和泛化性。