mlsanityData to 14 Oct 2026

Identification of COVID-19 related Fake News via Neural Stacking

arXiv 2101.03988 ↗First posted 11 Jan 2021cs.CL cs.LGcited by 2

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Abstract (arXiv)

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. urlhttps://gitlab.com/boshko.koloski/covid19-fake-news

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 11 Jan 2021.

Builds on

8 of 19 references.

References · 8

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  7. 2018Studying Fake News via Network Analysis: Detection and Mitigationcited by 14
  8. 2019tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classificationcited by 5

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.