Volume V | Issue 7 | 2026 | Paper ID: IJATEM-V05I07P1

An Adaptive Conv-Transformer Network with Whale Optimization for Social Media Engagement Classification

Author(s)

, R. Sahila Devi

Abstract

Social media engagement classification uses data tools to sort user actions like likes, shares, and comments. This process helps brands understand customer behaviour, deliver personal content, and build stronger connections. In this paper, an Adaptive Conv-Transformer Network (ACTN) with Whale Optimization Algorithm (WOA) is proposed for social media engagement classification. Feature transformation is a data preprocessing step that applies mathematical functions to change raw data values into a new format. Data visualization turns complex social media metrics into clear graphics. It helps sort user interactions like likes, shares, and comments into distinct engagement classes. An ACTN extracts local spatial patterns and global contextual dependencies from text, images, or user interaction sequences, classifying social media engagement levels (such as high, medium, or low interaction) with high accuracy. The WOA improves social media engagement classification by acting as a smart feature selector and hyperparameter tuner. The performance analysis is carried out using python software and assesses the model's performance using key metrics such as precision, recall, F1-score, confusion matrix, and ROC-AUC. The proposed ACTN-WOA uses four classes, they are high, low, medium and viral. The proposed ACTN-WOA achieves high accuracy of 96% and better convergence speed. The proposed method outperforms conventional techniques, contributing a robust and reliable tool for social media engagement classification.

Keywords

Adaptive Conv-Transformer Network (ACTN), Whale Optimization Algorithm (WOA), Social media engagement classification.

References

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An Adaptive Conv-Transformer Network with Whale Optimization for Social Media Engagement Classification

Author(s)

Abstract

Keywords

References

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