Few Labels with Active Learning: From Weak to Strong Labels for Misinformation Detection

Social media has evolved into a platform where individuals can freely disseminate content online.However, this unrestricted environment has unfortunately led to its misuse, with these platforms increasingly being used to circulate inappropriate content, misinformation, and disinformation.Misinformation detection becomes a crucial task in seeking social safety.In this work, we propose a framework called Few Labels with Active Learning (FLAL), which leverages a margin-sampling technique within the active learning paradigm.This enables the model to prioritize and learn from the most informative yet uncertain instances, enhancing its performance by focusing iteratively on examples that contribute the most to learning.As a result, the need for extensive labelling efforts is reduced.We evaluate FLAL across four multilingual benchmark datasets, where it demonstrates competitive results despite utilizing only a few labelled samples.To the best of our knowledge, this is the first study to apply active learning in conjunction with the few-label paradigm to data derived from an Arabic-language context.