  Text-embedded images can serve as a means of spreading hate speech,
propaganda, and extremist beliefs. Throughout the Russia-Ukraine war, both
opposing factions heavily relied on text-embedded images as a vehicle for
spreading propaganda and hate speech. Ensuring the effective detection of hate
speech and propaganda is of utmost importance to mitigate the negative effect
of hate speech dissemination. In this paper, we outline our methodologies for
two subtasks of Multimodal Hate Speech Event Detection 2023. For the first
subtask, hate speech detection, we utilize multimodal deep learning models
boosted by ensemble learning and syntactical text attributes. For the second
subtask, target detection, we employ multimodal deep learning models boosted by
named entity features. Through experimentation, we demonstrate the superior
performance of our models compared to all textual, visual, and text-visual
baselines employed in multimodal hate speech detection. Furthermore, our models
achieve the first place in both subtasks on the final leaderboard of the shared
task.
