A Novel Stance based Sampling for Imbalanced Data
Isha Y. Agarwal, Dipti P. Rana, Aemie Jariwala, Sahil Bondre · International Journal of Advanced Computer Science and Applications · 2022
While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector.