Identification of COVID-19 related Fake News via Neural Stacking
This work addresses the problem of identifying COVID-19 related fake news, which is important for public health and daily life during the pandemic, offering an incremental improvement in detection capability.
The paper describes a solution for COVID-19 fake news detection in English, achieving 50th place out of 168 submissions in a shared task, with its performance being within 1.5% of the top solution.
Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life. In this work we present a solution to the shared task titled COVID19 Fake News Detection in English, scoring the 50th place amongst 168 submissions. The solution was within 1.5% of the best performing solution. The proposed solution employs a heterogeneous representation ensemble, adapted for the classification task via an additional neural classification head comprised of multiple hidden layers. The paper consists of detailed ablation studies further displaying the proposed method's behavior and possible implications. The solution is freely available. \url{https://gitlab.com/boshko.koloski/covid19-fake-news}