Customer Engagement Prediction on Social Media: A Graph Neural Network Method

• 成果类型: Article
• 署名作者: Ma, Tengteng; Hu, Yuheng; Lu, Yingda; Bhattacharyya, Siddhartha
• 署名单位: State University System of Florida; University of South Florida; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.0281
• 发表日期: 2025
• 关键词: decision-support-system; dynamic-model; BEHAVIOR; facebook; brands

摘要

With the rapid prevalence and massive user growth of social media platforms, efficiently targeting potential customers on these platforms has grown in importance for companies. Enhancing the likelihood that a social media user will engage with brand posts holds profound implications for online marketing strategy design. However, predicting customer engagement on social media comes with its own set of challenges. In this work, we design a graph neural network model called the graph neural network with attention mechanism for customer engagement (GACE) to predict customer engagement (like/comment/share) of brand posts. We exploit large-scale content consumption information from the perspective of heterogeneous networks and learn latent customer representation by developing a graph neural network model. We examine GACE using a large-scale Face-book data set, and the comprehensive results show significant performance improvement over state-of-the-art baselines. Furthermore, we conduct an interpretability analysis, which sheds some light on the explanation of the proposed model. To illustrate the practical significance of our work, we provide examples to quantify the economic value of improved predictive power using a cost-revenue analysis in the context of targeted marketing.