Named Entity Recognition for Identifying Entities Related to Illegal Migration in Libya: An Analysis of Twitter Textual Data

The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entity Recognition (NER) is a natural language processing technique used to extract information from unstructured text, including identifying named entities such as people, organizations, and locations. This study aims to contribute to the identification of entities within collected textual data from Twitter related to illegal migration associated with Libya. The study will employ data mining tools, particularly pre-trained NER models to achieve its objective. The research is significant as there is currently no Libyan study that has addressed this specific subject using this technique. NER can help researchers, policymakers, law enforcement agencies, and humanitarian organizations to better understand the scope, dynamics, and impact of this complex issue.