Adversities are all you need: Classification of self-reported breast cancer posts on T witter using Adversarial Fine-tuning (2021)

Abstract: In this paper, we describe our system entry for Shared Task 8 at SMM4H-2021, which is on automatic classification of self-reported breast cancer posts on Twitter. In our system, we use a transformer-based language model fine-tuning approach to automatically identify tweets in the self-reports category. Furthermore, we involve a Gradient-based Adversarial fine-tuning to improve the overall model’s robustness. Our system achieved an F1-score of 0.8625 on the Development set and 0.8501 on the Test set in Shared Task-8 of SMM4H-2021.

## introduction with increased cases of discontinuation of breast cancer treatment, which often leads to cancer recurrence, there is a need to explore complementary sources of information for patient-centeredoutcomes(pcos) associated with breast cancer treatments. social media is a promising resource but extracting true pcos from it first requires the accurate detection of self-reported breast cancer patients. @xcite presented an nlp architecture along with a dataset for automatically categorising self-reported breast cancer posts. their dataset was released as shared task-8 in smm4h 2021. in this paper, we describe our system to automatically distinguish self-reports of breast cancer from non-relevant tweets, which we used in the final submission for the shared task-8 of smm4h-2021 (our best submission). ## conclusion in this paper, we described our approach of adversarial fine-tuning on domain-specific pre-trained model for classification of tweets as self-reports or not, which we used in our best submission at smm4h-2021, shared task 8. our ablation study demonstrates the usefulness of adversarial finetuning in improving the robustness of the model.