Identification Of Multilingual Offense and Troll From Social Media Memes Using Weighted Ensemble Of Multimodal Features

The paper "Identification of Multilingual Offense and Troll from Social Media Memes Using Weighted Ensemble of Multimodal Features" presents a novel approach to automatically detect offensive and trolling content in social media memes across multiple languages. The proposed method utilizes a combination of textual, visual, and audio features extracted from memes, and employs a weighted ensemble of machine learning classifiers to achieve improved accuracy. The results demonstrate the effectiveness of the proposed approach, which outperforms existing state-of-the-art methods in terms of F1-score. This approach has important implications for online moderation and the prevention of harmful content in multilingual social media environments. The results demonstrate that the proposed method achieves high accuracy in identifying offensive and troll behavior in multilingual social media memes. The findings of this study have important implications for social media monitoring and content moderation.

KEYWORDS: Mobile net