Understanding the Influence Impact of Social Media on Drug Addiction: A Novel Sentiment Analysis Approach Using Multi-Level User Engagement Data - Alnawas, Anwar; Alharbi, Hasanein; Al-Jawad, Mohammed (Iraqi Journal for Electrical and Electronic Engineering, 2025)
Drug addiction remains one of the key problems, which troubles each nation nowadays. Though social and economic factors have been contributing to its escalation significantly, recently in recent years a marked rise with drug addiction has witnessed in Iraq. Governments and societies are therefore working hard to find ways of counteracting this trend. Notably, social media networks have become major conduits of the dissemination sensitization about the risks involved in substance abuse addiction as well as consequences that are faced by drug abusers users. On the other hand, there are no studies analyzing user’s sentiment regarding drug addiction on social media in Iraq. This paper offers a new approach to fill this gap by presenting an analytical framework for identifying such sentiments of people from posts published on different popular platforms including Facebook and Twitter. In order to achieve this, a new dataset was generated from one of the relevant Facebook pages and comprised three distinct levels of user engagement data. Our goal is to create a direct connection between the research objectives and practical applications which can benefit society. This study’s results contribute significantly to the understanding of sentimental movements regarding drug addiction and affect public perceptions on this significant problem. This study makes contributions to such fields are sentiment analysis, social media research and public health by revealing the complex interaction of social media itself, user’s feelings towards it or even drug addiction in Iraq. The new approach to analysis of multi-level user engagement data and offers an evidence based solution for dealing with the challenges presented by drug abuse in society. Using a neural network algorithm, the classification model developed has shown excellent performance with an accuracy rate of about 91%.