Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

Fleming, Max; Dondeti, Priyanka; Dreisbach, Caitlin; Poliak, Adam

doi:10.18653/v1/2021.smm4h-1.28

Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task 2021

DOI: 10.18653/v1/2021.smm4h-1.28

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Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

Max Fleming¹,

Priyanka Dondeti²,

Caitlin Dreisbach³

et al.

Abstract: We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill-BERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Ta… Show more

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Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

Magge¹,

Klein²,

Miranda-Escalada³

et al. 2021

Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

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The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health. The Social Media Mining for Health Research and Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multiclass classification, named entity recognition (NER) and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.

show abstract

Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

Magge¹,

Klein²,

Miranda-Escalada³

et al. 2021

Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

View full text Add to dashboard Cite

show abstract

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Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

Cited by 1 publication

References 6 publications

Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

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