Predicting consumer engagement with virtual broadcasters by combination of AlexNet, Inceptionv4, and Enhanced Addax Optimization Algorithm in social media marketing

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Virtual YouTubers (VTubers) have reimagined and reorganized digital audiences by predicting live viewer interaction, a task made tricky by the noisy and bursty nature of livechat data. The present study offers a new architecture, a hybridization of AlexNet and Inception-v4 as a two-branch CNN, trained using an Enhanced version of the Addax Optimization Algorithm with quasi-oppositional and chaotic search dynamics. When applied to the VTuber 1B dataset (more than 1 billion events), the proposed model achieves state-of-the-art results with MAE = 0.082, RMSE = 0.119, and R² = 0.843, significantly outperforming nine baselines such as XGBoost, LSTM, and Transformer variants. The architecture effectively captures both impulsive phrase triggers and sustained discourse patterns, enabling real-time engagement forecasting. This paper bridges the fields of computational marketing and deep learning, providing a deployable solution for influencer analytics and live-stream monetization strategies within social media ecosystems.

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