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A user-generated text on social media enables health workers to keep track ofinformation, identify possible outbreaks, forecast disease trends, monitoremergency cases, and ascertain disease awareness and response to officialhealth correspondence. This exchange of health information on social media hasbeen regarded as an attempt to enhance public health surveillance (PHS).Despite its potential, the technology is still in its early stages and is notready for widespread application. Advancements in pretrained language models(PLMs) have facilitated the development of several domain-specific PLMs and avariety of downstream applications. However, there are no PLMs for social mediatasks involving PHS. We present and release PHS-BERT, a transformer-based PLM,to identify tasks related to public health surveillance on social media. Wecompared and benchmarked the performance of PHS-BERT on 25 datasets fromdifferent social medial platforms related to 7 different PHS tasks. Comparedwith existing PLMs that are mainly evaluated on limited tasks, PHS-BERTachieved state-of-the-art performance on all 25 tested datasets, showing thatour PLM is robust and generalizable in the common PHS tasks. By making PHS-BERTavailable, we aim to facilitate the community to reduce the computational costand introduce new baselines for future works across various PHS-related tasks.

Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model