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}

翻译：假消息的识别在目前的大流行病中起着突出作用,影响到日常生活的多个方面。在这项工作中,我们提出了一个共同任务的解决方案,即英文的COVID19假消息探测,在168份呈件中排名第50位。解决方案在最佳效果解决方案的1.5%之内。拟议解决方案采用多种代表组合,通过由多个隐藏层组成的额外神经分类头来适应分类任务。文件包含详细的通货膨胀研究,进一步展示了拟议方法的行为和可能的影响。解决方案可以自由获取。\url{https://gitlab.com/boshko.kolski/covid19-fake-news}