Computers, Vol. 15, Pages 749: Evolutionary Random Weight Networks For Effective Offensive Language Detection In Arabic Social Media: Insights For Business Media And Intelligence

Computers, Vol. 15, Pages 749: Evolutionary Random Weight Networks for Effective Offensive Language Detection in Arabic Social Media: Insights for Business Media and Intelligence

Computers doi: 10.3390/computers15110749

Authors: Enas Rawashdeh Rawan Zaghloul Ghada Alrawashdeh Tomader Bani-Ata

Online social networks have become a crucial medium for global communication, with many users accessing their accounts multiple times a day. This trend was particularly pronounced during the Covid-19 crisis, as quarantines and extended periods at home drove a significant increase in online activity. However, this surge in usage also brought about negative societal behaviors, such as heightened instances of offensive language, hate speech, and online bullying. Therefore, detecting these abuses electronically has become necessary, despite the slowness that accompanies manual detection, particularly with large data. This paper proposes a model for the automatic identification of offensive language in Arabic tweets using fine-tuned word embedding models. The proposed GWO-RWN model combines a Random Weight Network (RWN) as the classifier with a Grey Wolf Optimizer (GWO) to optimize the RWN configuration. Experiments were conducted on the ArCybC dataset of 4505 Arabic tweets, using an 60% training, 20% validation and 20% testing split. Word2Vec embeddings with CBOW and Skip-G architectures and GloVe embeddings were evaluated, and the proposed GWO-RWN was compared with GA-, PSO-, and ABC-optimized RWN models over 30 independent runs. The proposed approach achieved an accuracy of 90.5%, with precision, recall, and F1-score of 0.891, 0.888, and 0.889, respectively, using Skip-G embeddings. This represents a 4.8-percentage-point improvement over the non-optimized RWN, which achieved an accuracy of 85.7%.This exceeds previously reported results on the same dataset, including 88.2% accuracy with a GA-optimized SVM and 86.3% accuracy with an SVM using word embeddings.