Lexical Squad@Multimodal Hate Speech Event Detection 2023: Multimodal
  Hate Speech Detection using Fused Ensemble Approach
Mohammad Kashif Mohammad Zohair Saquib Ali
With a surge in the usage of social media postings to express opinions,
emotions, and ideologies, there has been a significant shift towards the
calibration of social media as a rapid medium of conveying viewpoints and
outlooks over the globe. Concurrently, the emergence of a multitude of
conflicts between two entities has given rise to a stream of social media
content containing propaganda, hate speech, and inconsiderate views. Thus, the
issue of monitoring social media postings is rising swiftly, attracting major
attention from those willing to solve such problems. One such problem is Hate
Speech detection. To mitigate this problem, we present our novel ensemble
learning approach for detecting hate speech, by classifying text-embedded
images into two labels, namely "Hate Speech" and "No Hate Speech". We have
incorporated state-of-art models including InceptionV3, BERT, and XLNet. Our
proposed ensemble model yielded promising results with 75.21 and 74.96 as
accuracy and F-1 score (respectively). We also present an empirical evaluation
of the text-embedded images to elaborate on how well the model was able to
predict and classify. We release our codebase here
(https://github.com/M0hammad-Kashif/MultiModalHateSpeech).