Offensive Language and Image Identification on Social Media Based on Text and Image Classification

Abstract

A digital signature is like a digital version of a handwritten signature but much more secure. It ensures that digital documents are authentic, unaltered, and genuinely from the sender. Our project, Digital Signature Tool, focuses on creating an easy-to-use application for securely signing and verifying documents. Using advanced cryptographic methods like RSA or ECDSA, the tool allows users to generate and manage private and public keys securely. To sign a document, the sender uses their private key to create a unique digital signature, while the receiver uses the sender’s public key to verify the signature. This process confirms the document\'s authenticity and ensures it has not been tampered with. The application will integrate essential features, such as secure key management, document signing, and signature verification, all within a user-friendly interface. This project aims to provide individuals and organizations with a reliable solution for protecting their documents and communications, ensuring trust, data integrity, and security in the digital space.

Introduction

1. Introduction

Social media platforms have become central to communication, but they also host offensive, hateful, and abusive content. Detecting such harmful material—especially when it combines text and images—is challenging due to the complexity of language, context, and visual cues.

2. Research Objective

The study proposes an integrated framework using Natural Language Processing (NLP) and Computer Vision (CV) to detect offensive content more accurately. It leverages deep learning models like BERT for text and CNNs (e.g., ResNet, VGG) for images. It emphasizes the use of multimodal models that analyze both visual and textual inputs together for better context understanding.

3. Literature Review

• Text-Based Detection:

  • Early methods used keyword matching and handcrafted features, but lacked context.

  • Deep learning models (CNNs, LSTMs) improved contextual understanding.

  • Transformers (e.g., BERT, RoBERTa) brought state-of-the-art accuracy using attention mechanisms.

  • Datasets like OLID and Davidson et al.'s Twitter dataset are key benchmarks.

• Image-Based Detection:

  • CNNs revolutionized visual content classification.

  • Models like VGGNet, ResNet, InceptionNet are widely used.

  • Transfer learning helps with limited data.

  • Datasets such as Hateful Memes and ToxiImageNet aid in training on offensive imagery.

• Multimodal Approaches:

  • Multimodal models (e.g., VisualBERT, VilBERT, CLIP) analyze both image and text.

  • These systems outperform unimodal models in detecting context-dependent or sarcastic content.

  • Challenges include annotation difficulty, interpretability, and cultural generalization.

4. Key Success Factors

• Dataset Quality: Diverse and well-labeled datasets are critical, especially those with both image and text inputs.

• Preprocessing: Text cleaning, emoji handling, sarcasm detection, and image augmentation improve model performance.

• Model Selection: Using robust models like BERT (text) and ResNet (images), or multimodal models for fusion.

• Feature Fusion: Methods for combining image and text features (early, late, or hybrid fusion) impact context understanding.

• Evaluation Metrics: Accuracy, precision, recall, F1-score, and ROC-AUC ensure reliable model validation.

• Context Awareness: Recognizing subtle, sarcastic, or implicit content is vital.

• Real-Time Performance: The model must scale and perform efficiently on live social media data.

• Ethical Considerations: Avoiding algorithmic bias based on race, gender, or language is crucial.

• Continual Learning: The model should adapt to evolving slang and trends in offensive content.

5. Outputs

The system includes:

• Text Input Interface for submitting content

• Offensive Text Classification Result

• Image Classification Result

Conclusion

In today\'s digital age, social media platforms are increasingly becoming hotspots for the dissemination of offensive and harmful content. The proposed system leverages the power of machine learning and deep learning to address this critical issue by classifying both textual and visual content for offensiveness. By implementing separate models for text and image analysis, the system ensures a comprehensive approach to offensive content detection. The results demonstrate that the system is capable of accurately identifying non-offensive and offensive elements in user-generated content. However, instances of false positives in text classification, such as labeling neutral sentences as offensive, indicate the need for further refinement and training on more diverse datasets. Image classification performed well, showing potential in recognizing offensive visuals, thereby enhancing the overall effectiveness of the tool. This research highlights the feasibility of developing automated tools for real-time moderation of social media content. With further improvements in data diversity, model tuning, and the incorporation of multimodal analysis (combining text and image in a single context), the system can be a valuable asset in combating cyberbullying, hate speech, and the spread of harmful content online. Ultimately, this project contributes to a safer digital ecosystem by empowering platforms and users with intelligent tools that promote respectful and inclusive online interactions.

References

[1] Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Predicting the Type and Target of Offensive Posts in Social Media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 1415–1420. [2] Schmidt, A., &Wiegand, M. (2017). A Survey on Hate Speech Detection using Natural Language Processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pp. 1–10. [3] Basile, V., Bosco, C., Fersini, E., et al. (2019). SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 54–63. [4] Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. In Proceedings of ICWSM 2017. [5] Jaiswal, A., &Goel, A. (2021). Image-based Offensive Content Detection using CNN Models. International Journal of Computer Applications, Vol. 182(17), pp. 25–30. [6] Goodfellow, I., Bengio, Y., &Courville, A. (2016). Deep Learning. MIT Press. [7] OpenAI (2020). GPT Language Models. https://openai.com/research [8] Facebook AI (2021). Hateful Memes Challenge Dataset. https://ai.facebook.com/hatefulmemes [9] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS), pp. 1097–1105. [10] TensorFlow (2023). Open Source Machine Learning Framework. https://www.tensorflow.org

Copyright

Copyright © 2025 G. Mahammad Idrush, Ch. Sai Maharsha, A. Gangaprasad, A. Sai Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.