Detecting indicators of violence in digital text using deep learning

Abstract

Individuals who experience violence often use digital platforms to share their experiences and find assistance. Artificial intelligence (AI) techniques have emerged as one of the successful technological strategies used for the detection of indicators of violence in various forms of data, particularly text communications. A hybrid deep learning model is introduced in this paper for the detection of violence indicators in online text communications. It enables the extraction of word embeddings from texts to infer the contextual relationships among words. Additionally, it uses a classifier capable of processing sequential data in both forward as well as backward directions. This approach enables the retention of long-term dependencies from texts while maintaining semantic relationships between words. The word embeddings extraction is implemented with the use of the bidirectional encoder representations from transformer algorithm. The sequence processing classification is implemented by incorporating a combination of parallel layers consisting of the bidirectional long–short-term memory as well as the bidirectional gated recurrent unit algorithms. The developed deep learning architecture is experimentally tested, and the associated results are compared with those of several other machine learning models. The findings are presented and discussed.