Hate Speech Detection Using Deep Learning Algorithms

The surge in hate speech on social media, notably platforms like Twitter, is a mounting concern due to its potential harm to individuals and entire communities, fostering toxicity, discrimination, and violence. Primarily targeting protected categories such as gender, religion, and disability, hate speech also extends to characteristics like sexual orientation and political affiliation. Mitigating this issue requires robust measures, and the proposed methodology offers a multifaceted approach. The methodology uses natural language processing (NLP) techniques like sentiment analysis, and topic modelling to discern potentially harmful content. Advanced deep learning algorithms such as Random Forest, Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN), are then employed for hate speech classification and filtration. The resultant web application framework provides social media companies with a proactive tool to monitor and eliminate hate speech. The methodology uses NLP techniques and machine learning algorithms such as Random Forest (accuracy: 0.79%), deep learning algorithms like LSTM (accuracy: 0.68%), and RNN (accuracy: 1.0%), collectively offering a promising solution. Beyond technical interventions, it is crucial to promote awareness and education about the harmful effects of hate speech. Working and coordinating with community leaders, advocacy groups, and educators, social media platforms can provide resources and training on respectful communication, diversity, and inclusion. Transparent communication with users about hate speech policies and enforcement actions further fosters a culture of respect online. Overall, the proposed methodology, coupled with educational efforts, aims to create a safer, more inclusive online community. By synergizing technical solutions and fostering awareness, it aims to mitigate the adverse impacts of hate speech, promoting a positive and respectful online environment for all users.