  Social media in present times has a significant and growing influence. Fake
news being spread on these platforms have a disruptive and damaging impact on
our lives. Furthermore, as multimedia content improves the visibility of posts
more than text data, it has been observed that often multimedia is being used
for creating fake content. A plethora of previous multimodal-based work has
tried to address the problem of modeling heterogeneous modalities in
identifying fake content. However, these works have the following limitations:
(1) inefficient encoding of inter-modal relations by utilizing a simple
concatenation operator on the modalities at a later stage in a model, which
might result in information loss; (2) training very deep neural networks with a
disproportionate number of parameters on small but complex real-life multimodal
datasets result in higher chances of overfitting. To address these limitations,
we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework
that allows granular interactions within and across different modalities to
learn more robust data representations for multimodal fake news detection. We
use two publicly available fake news datasets, Twitter and Weibo, for
evaluations. Our model outperforms on Twitter by an average of 11% and keeps
competitive performance on Weibo, within a 2.6% margin, while using 65% fewer
parameters than the best comparable state-of-the-art baseline.
