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arXiv · 2101.03988

Identification of COVID-19 related Fake News via Neural Stacking

Boshko Koloski·Timen Stepišnik Perdih·Senja Pollak·Blaž Škrlj
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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}

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Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, Blaž Škrlj. 2021-01-11. Identification of COVID-19 related Fake News via Neural Stacking. https://doi.org/10.1007/978-3-030-73696-5_17

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