  Fake News Detection (FND) is an essential field in natural language
processing that aims to identify and check the truthfulness of major claims in
a news article to decide the news veracity. FND finds its uses in preventing
social, political and national damage caused due to misrepresentation of facts
which may harm a certain section of society. Further, with the explosive rise
in fake news dissemination over social media, including images and text, it has
become imperative to identify fake news faster and more accurately. To solve
this problem, this work investigates a novel multimodal stacked ensemble-based
approach (SEMIFND) to fake news detection. Focus is also kept on ensuring
faster performance with fewer parameters. Moreover, to improve multimodal
performance, a deep unimodal analysis is done on the image modality to identify
NasNet Mobile as the most appropriate model for the task. For text, an ensemble
of BERT and ELECTRA is used. The approach was evaluated on two datasets:
Twitter MediaEval and Weibo Corpus. The suggested framework offered accuracies
of 85.80% and 86.83% on the Twitter and Weibo datasets respectively. These
reported metrics are found to be superior when compared to similar recent
works. Further, we also report a reduction in the number of parameters used in
training when compared to recent relevant works. SEMI-FND offers an overall
parameter reduction of at least 20% with unimodal parametric reduction on text
being 60%. Therefore, based on the investigations presented, it is concluded
that the application of a stacked ensembling significantly improves FND over
other approaches while also improving speed.
