Examining Deep Learning Techniques for Ethical Artificial Intelligence: Cleansing Malicious Comments from Users
The advancement of AI has heightened the significance of ethical concerns, particularly in managing negative user feedback like malicious comments, necessitating thoughtful deliberation. The focus of this research is to explore the potential of deep learning techniques in addressing these issues and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2025-06-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/128450/download/pdf/ |
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| Summary: | The advancement of AI has heightened the significance of ethical concerns, particularly in managing negative user feedback like malicious comments, necessitating thoughtful deliberation. The focus of this research is to explore the potential of deep learning techniques in addressing these issues and enhancing the ethical nature of AI systems. Specifically, we investigated the collection and processing of news comment data using Long Short-Term Memory (LSTM) algorithm and Word2Vec model. The primary objective was to evaluate how deep learning techniques can improve the quality of data obtained from news comments. Our findings demonstrate that deep learning models surpass CleanBot in accuracy and block rates for handling negative user comments, including malicious ones, enabling organizations to effectively manage such comments in online communities using AI-based methods. This study adds to the existing research by showing how advanced deep learning techniques can effectively identify and classify harmful comments by analyzing complex language patterns. |
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| ISSN: | 0948-6968 |