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
(this https URL).

我们提出了一种新颖的集成学习方法来检测仇恨言论，通过将文本嵌入图像分类为 “仇恨言论” 和 “非仇恨言论” 的两个标签。我们使用了 InceptionV3、BERT 和 XLNet 等现代模型，该集成模型在准确度和 F-1 分数方面取得了有希望的结果（分别为 75.21 和 74.96）。我们还对文本嵌入图像进行了实证评估，并在此处发布我们的代码库。