Community Detection and Hate Speech Analysis on Twitter Using a Hybrid LSTM-GRU Model

This study presents a novel approach for detecting hate speech and identifying influential users on Twitter, aiding policymakers and law enforcement in controlling its spread. The proposed method employs a hybrid LSTM-GRU model to classify hate speech into six categories. Additionally, the Girvan-Newman algorithm is used to uncover hidden communities and key influencers within the network. Achieving an accuracy of ۹۸.۳۲%, the model significantly outperforms traditional architectures such as LSTM-CNN, LSTM-Stacked, and standard LSTM. The results highlight the model’s high effectiveness in accurately detecting hate content and identifying the main individuals responsible for its dissemination.

کلیدواژه ها:

Deep Learning ، Social Network Analysis ، Community Identification ، Long-short-term Memory