MemeCheck: Automated Meme Analysis for Identifying Offensive Text and Visuals

Jaganath M., Jothika R., Keerthika S., Pranishka N., Vinoharsitha A. S.

2025

Abstract

As a model that has emerged as a key aspect in the field and foundation of discovering Human emotions and sentiment in the digital world. Given that memes are much meme owe popular than smoke signals, they seem to cover most feelings in a funky pictoral way. The popularity of multimodal content, which often involves combining text with images, has also been the subject of much scrutiny in online communication for the potential amplification of hateful and damaging content. In such a regard, this study introduces a brand-new method for branded offensive memes classification relying on deep learning strategies to deal with multimodal datasets. To accurate identify and categorise inappropriate contents. It is crucial to conduct analysis both textual and visual components. In this study, we propose a novel approach for sentiment analysis from meme images using Optical Character Recognition (OCR) technology combined with deep learning algorithms for text classification and image classification. Our method consists of performing OCR on meme images to extract text content for dissection in textual analysis and drawing inferences for sentiment induction. The analyzed text, utilizing VADER and NLP, takes the next step by deducing sentiments based on the recognized text. The same results are used to take detects any kind of offensive content from annoyingly unwanted images through a Sequential CNN model. This very particular CNN was modelled and has been trained from scratch for the accuracy of classifying offensive images and enhancing the solution of recognizing inappropriate visual content. With this novel multimodal technique, detection of offensive content from text and image is done efficiently, providing a safer and more responsible platform in social networking. The proposed methodology shows good promise in introducing an effective OCR-driven VADER sentiment analysis and performing Sequential CNN-based image classification for both the identification and control of offensive content on social networks.

Paper Citation

in Harvard Style

M. J., R. J., S. K., N. P. and S. V. (2025). MemeCheck: Automated Meme Analysis for Identifying Offensive Text and Visuals. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 676-682. DOI: 10.5220/0013918900004919

in Bibtex Style

@conference{icrdicct`2525,
author={Jaganath M. and Jothika R. and Keerthika S. and Pranishka N. and Vinoharsitha S.},
title={MemeCheck: Automated Meme Analysis for Identifying Offensive Text and Visuals},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={676-682},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013918900004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - MemeCheck: Automated Meme Analysis for Identifying Offensive Text and Visuals
SN - 978-989-758-777-1
AU - M. J.
AU - R. J.
AU - S. K.
AU - N. P.
AU - S. V.
PY - 2025
SP - 676
EP - 682
DO - 10.5220/0013918900004919
PB - SciTePress