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With a surge in the usage of social media postings to express opinions,emotions, and ideologies, there has been a significant shift towards thecalibration of social media as a rapid medium of conveying viewpoints andoutlooks over the globe. Concurrently, the emergence of a multitude ofconflicts between two entities has given rise to a stream of social mediacontent containing propaganda, hate speech, and inconsiderate views. Thus, theissue of monitoring social media postings is rising swiftly, attracting majorattention from those willing to solve such problems. One such problem is HateSpeech detection. To mitigate this problem, we present our novel ensemblelearning approach for detecting hate speech, by classifying text-embeddedimages into two labels, namely "Hate Speech" and "No Hate Speech". We haveincorporated state-of-art models including InceptionV3, BERT, and XLNet. Ourproposed ensemble model yielded promising results with 75.21 and 74.96 asaccuracy and F-1 score (respectively). We also present an empirical evaluationof the text-embedded images to elaborate on how well the model was able topredict and classify. We release our codebase here(https://github.com/M0hammad-Kashif/MultiModalHateSpeech).

Lexical Squad@Multimodal Hate Speech Event Detection 2023: Multimodal Hate Speech Detection using Fused Ensemble Approach