Assessing the Influence of Advertisements on Social Interactions in Iraqi Dialect WhatsApp Groups Using BERT

Abstract

The widespread use of instant messaging applications, such as WhatsApp, has converted these platforms into dynamic venues for social interaction. The rising prevalence of commercial advertisements may negatively affect the quality of social connections. This study investigates the impact of ads on social interactions in WhatsApp groups using the Iraqi dialect, applying natural language processing and artificial intelligence techniques. We propose a methodology employing the BERT model to classify WhatsApp messages in the Iraqi dialect into three primary categories: advertisements, social discourse, and neutral communications. The primary objective is to assess the impact of advertisements on the dynamics of discussions among users in groups.  A dataset comprising 5,000 messages was meticulously gathered and categorized into two classifications: advertisements and social discussions. The pre-trained CAMeLBERT model underwent fine-tuning on this dataset by incorporating a classification head and training for 50 epochs with a batch size of 8 and a learning rate 2e-5.Experimental results indicate that the model attained an F1-score of 97%, effectively differentiating between commercial communications and casual conversations. Approximately 35% of the messages were classified as promotional content. A conventional SVM model utilising TF-IDF features was implemented to assess performance, attaining merely 81.3% accuracy, underscoring the superiority of the transformer-based methodology. These findings indicate that the increasing prevalence of advertisements may discreetly disrupt the natural flow of conversations in digital communities, necessitating the implementation of sophisticated filtering systems.