Identification of Offensive content from Social media

The paper introduces a system designed to detect offensive text embedded in social media images. With the rise of user-generated content, harmful behaviors like hate speech and cyberbullying have become more prevalent, particularly within images and memes. To address this, the proposed system combines deep learning and natural language processing techniques to identify and classify toxic content. Index Terms—Deep learning, YOLO

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-23
DOI
https://doi.org/10.5281/zenodo.15538607
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Identification of Offensive content from Social media

Zenodo (CERN European Organization for Nuclear Research)
Hate Speech and Cyberbullying Detection
article

Identification of Offensive content from Social media

article en

Abstract

The paper introduces a system designed to detect offensive text embedded in social media images. With the rise of user-generated content, harmful behaviors like hate speech and cyberbullying have become more prevalent, particularly within images and memes. To address this, the proposed system combines deep learning and natural language processing techniques to identify and classify toxic content. Index Terms—Deep learning, YOLO

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 76%
Hate Speech and Cyberbullying Detection
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Identification of Offensive content from Social media · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS