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This paper presents models created for the Social Media Mining for Health2023 shared task. Our team addressed the first task, classifying tweets thatself-report Covid-19 diagnosis. Our approach involves a classification modelthat incorporates diverse textual augmentations and utilizes R-drop to augmentdata and mitigate overfitting, boosting model efficacy. Our leading model,enhanced with R-drop and augmentations like synonym substitution, reservedwords, and back translations, outperforms the task mean and median scores. Oursystem achieves an impressive F1 score of 0.877 on the test set.

Text Augmentations with R-drop for Classification of Tweets Self Reporting Covid-19