DOI: 10.17350/hjse19030000381 ISSN: 2148-4171

A New Framework for Decentralized Social Networks: Harnessing Blockchain, Deep Learning, and NLP

Amir Al Kadah, Deniz Balta
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This paper presents a novel framework for a decentralized social network that integrates blockchain technology, natural language processing (NLP), and deep learning (DL) to address critical vulnerabilities in traditional centralized online social networks (OSNs). Blockchain ensures data integrity, transparency, and decentralized governance, mitigating risks associated with data manipulation and privacy breaches. Deep learning algorithms, including Bidirectional LSTM for post-category prediction and LSTM for suicide detection, enhance content management by capturing nuanced language cues and identifying distress signals. NLP techniques, such as TF-IDF vectorization and cosine similarity, further improve content originality and moderation by detecting duplicates, preventing plagiarism, and fostering diverse content. This paper also elaborates on the implementation of these technologies, demonstrating how blockchain-based smart contracts manage secure interactions, deep learning models categorize content, and NLP techniques ensure content authenticity. This comprehensive integration of blockchain, deep learning, and NLP offers a transformative approach to social networking, promoting transparency, security, and ethical standards, while creating a safer, more trustworthy digital environment.

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