An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media

Nath, Nilanjan; George, Jossy P.; Kesan, Athishay; Rodrigues, Andrea

doi:10.1007/978-981-19-2211-4_30

Cited by 2 publications

(1 citation statement)

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“…3) Bag of Words. Within the scope of computational linguistics, the Bag of Words (BoW) model stands as a simplified representation, used to preprocess the text by transforming it into a set of distinguishable words, or "tokens," thereby constructing a dictionary of the language used in the entire text corpus [57].…”

Section: A) Term Frequency (Tf)mentioning

confidence: 99%

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Kazbekova,

Ismagulova,

Kemelbekova

et al. 2023

IJACSA

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In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.

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Section: A) Term Frequency (Tf)mentioning

confidence: 99%