  The paper presents our solutions for the MediaEval 2020 task namely FakeNews:
Corona Virus and 5G Conspiracy Multimedia Twitter-Data-Based Analysis. The task
aims to analyze tweets related to COVID-19 and 5G conspiracy theories to detect
misinformation spreaders. The task is composed of two sub-tasks namely (i)
text-based, and (ii) structure-based fake news detection. For the first task,
we propose six different solutions relying on Bag of Words (BoW) and BERT
embedding. Three of the methods aim at binary classification task by
differentiating in 5G conspiracy and the rest of the COVID-19 related tweets
while the rest of them treat the task as ternary classification problem. In the
ternary classification task, our BoW and BERT based methods obtained an
F1-score of .606% and .566% on the development set, respectively. On the binary
classification, the BoW and BERT based solutions obtained an average F1-score
of .666% and .693%, respectively. On the other hand, for structure-based fake
news detection, we rely on Graph Neural Networks (GNNs) achieving an average
ROC of .95% on the development set.
