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.15538608
Primary Topic
Photosynthetic Processes and Mechanisms
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)
Photosynthetic Processes and Mechanisms
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)
Peace, Justice and strong institutions
Openalex Percentile: Top 55%
Photosynthetic Processes and Mechanisms
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Identification of Offensive content from Social media · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS