This paper presents our approaches for the SMM4H'24 Shared Task 5 on the binary classification of English tweets reporting children's medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

The authors have declared no competing interest.

This study did not receive any funding

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All datasets are composed entirely of tweets posted by users who had reported their pregnancy on Twitter, that report having a child with a disorder and tweets that merely mention a disorder. We obtained the dataset from the publication here :https://www.jmir.org/2024/1/e50652/PDF Data is publicly available at here: https://jmir.org/api/download?alt_name=jmir_v26i1e50652_app3.txt&filename=d4491eeb93a8ba9d82dfd364a3a4a8d1.txt

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All data produced are available online at: https://jmir.org/api/download?alt_name=jmir_v26i1e50652_app3.txt&filename=d4491eeb93a8ba9d82dfd364a3a4a8d1.txt