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Vol. 11, No. 3, August 2026 (Article in Progress)

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Predicting Social Media Post Engagement and Virality Using Graph Neural Network Approaches and Content-Based Features

https://doi.org/10.22219/kinetik.v11i3.2686
Fathimah Az Zahrah
State University of Surabaya
Riska Dhenabayu
State University of Surabaya
Muhammad Fajar Wahyudi Rahman
State University of Surabaya
Renny Sari Dewi
State University of Surabaya
Zamabhungane Hadebe Aminah
University of Johannesburg

Corresponding Author(s) : Fathimah Az Zahrah

fathimah.22119@mhs.unesa.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 3, August 2026 (Article in Progress)

Abstract

Social media teams increasingly rely on early signals to prioritize content, yet forecasting engagement and identifying viral posts remain difficult under temporal drift and heavy-tailed interaction counts. This study evaluated Graph Neural Network (GNN) approaches for predicting post engagement and virality from pre-posting content-based and contextual features. The Social Media Engagement Report dataset, which contained 100,000 posts across Twitter, LinkedIn, Facebook, and Instagram spanning March 2021–March 2024, was used. Post-release variables (impressions, reach, engagement rate) were excluded to prevent leakage. A homogeneous post–post graph was constructed using k-nearest-neighbor similarity in an embedding space and exact-match links on low-cardinality context. Ridge/Logistic Regression, Random Forest, and XGBoost as the baselines were compared against GraphSAGE and GAT under a chronological train, validation, and test split. Regression used MAE, RMSE, and R2, while virality classification used ROC-AUC, PR-AUC, and Precision at the top 1% ranked posts. GraphSAGE yielded the strongest virality screening, achieving ROC-AUC = 0.66, PR-AUC = 0.54–0.56, and Precision@1% up to 0.75, substantially above non-graph baselines. For regression, GAT produced the lowest errors despite a negative R², indicating limited explained variance. Overall, similarity-graph GNNs are most effective for early virality identification, whereas exact count prediction remains challenging in a strictly pre-posting, time-aware setting.

Keywords

GraphSAGE Graph Attention Network Virality Classification Engagement Regression Post Similarity Graph
Az Zahrah, F., Riska Dhenabayu, Muhammad Fajar Wahyudi Rahman, Renny Sari Dewi, & Zamabhungane Hadebe Aminah. (2026). Predicting Social Media Post Engagement and Virality Using Graph Neural Network Approaches and Content-Based Features. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(3). https://doi.org/10.22219/kinetik.v11i3.2686
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