Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Abstract

Hate speech on social media platforms like Twitter is a growing concern that poses challenges to maintaining a healthy online environment and fostering constructive communication. Effective detection and monitoring of hate speech are crucial for mitigating its adverse impact on individuals and communities. In this paper, we propose a comprehensive approach for hate speech detection on Twitter using both traditional machine learning and deep learning techniques. Our research encompasses a thorough comparison of these techniques to determine their effectiveness in identifying hate speech on Twitter. We construct a robust dataset, gathered from diverse sources and annotated by experts, to ensure the reliability of our models. The dataset consists of tweets labeled as hate speech, offensive language, or neutral, providing a more nuanced representation of online discourse. We evaluate the performance of LSTM, BiLSTM, and CNN models against traditional shallow learning methods to establish a baseline for comparison. Our findings reveal that deep learning techniques outperform shallow learning methods, with BiLSTM emerging as the most accurate model for hate speech detection. The BiLSTM model demonstrates improved sensitivity to context, semantic nuances, and sequential patterns in tweets, making it adept at capturing the intricate nature of hate speech. Furthermore, we explore the integration of word embeddings, such as Word2Vec and GloVe, to enhance the performance of our models. The incorporation of these embeddings significantly improves the models' ability to discern between hate speech and other forms of online communication. This paper presents a comprehensive analysis of various machine learning methods for hate speech detection on Twitter, ultimately demonstrating the superiority of deep learning techniques, particularly BiLSTM, in addressing this critical issue. Our findings pave the way for further research into advanced methods of tackling hate speech and facilitating healthier online interactions.

A comparative analysis of machine learning algorithms for hate speech detection in social media

“…Feature extraction was pivotal in transforming raw text data into meaningful numerical representations for machine learning algorithms (DeepAI, 2019). This study explored different feature extraction techniques, including n-grams, bag-of-words (BoW), and term frequency-inverse document frequency (TF-IDF), which play essential role for feature extraction (Toktarova et al, 2023). N-grams were employed to capture different levels of contextual information, ranging from individual words to pairs (bigrams) and triplets (trigrams) of words.…”