  With the ease of access to information, and its rapid dissemination over the
internet (both velocity and volume), it has become challenging to filter out
truthful information from fake ones. The research community is now faced with
the task of automatic detection of fake news, which carries real-world
socio-political impact. One such research contribution came in the form of the
Constraint@AAA12021 Shared Task on COVID19 Fake News Detection in English. In
this paper, we shed light on a novel method we proposed as a part of this
shared task. Our team introduced an approach to combine topical distributions
from Latent Dirichlet Allocation (LDA) with contextualized representations from
XLNet. We also compared our method with existing baselines to show that XLNet +
Topic Distributions outperforms other approaches by attaining an F1-score of
0.967.
