Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling

Autor:	Yoo, Clay H., KhudaBukhsh, Ashiqur R.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	This paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and topical diversity of the negative class (dubbed anticontent) as observed in the wild. As a result, these classifiers' strong performance observed on the test set may not translate into real-world settings. In the context of COVID-19 misinformation detection, we conduct an in-the-wild audit of multiple datasets and demonstrate that models trained with several prominently cited recent datasets are vulnerable to anticontent when evaluated in the wild. Second, we present a novel active learning pipeline that requires zero manual annotation and iteratively augments the training data with challenging anticontent, robustifying these classifiers. Comment: This paper has been accepted at AAAI 2023 (Robust and Safe AI track)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.07078 Zobrazit plný text záznamu View this record from Arxiv