Identification of COVID-19 related Fake News via Neural Stacking

TL;DR

This paper presents a neural stacking ensemble method for detecting COVID-19 related fake news, achieving competitive performance in a shared task with detailed ablation studies.

Contribution

It introduces a heterogeneous representation ensemble with a neural classification head for improved fake news detection during the pandemic.

Findings

Achieved 50th place out of 168 submissions

Within 1.5% of the best solution

Provides detailed ablation studies

Abstract

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}