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<p>Employee digital strain has emerged as a critical factor affecting workplace productivity and well-being especially in the modern fast paced work environment. Accurately detecting overwork, multitasking, and burnout requires modeling complex behavioral and relational dependencies from multimodal digital activity data. In this work, we propose AutoEG, an automated graph-based contrastive learning framework for robust employee stress representation and classification. Layer 1 comprises <i>multimodal behavioral encoder (MBE)</i> which extracts embeddings from keystrokes, application usage logs, and chat/email sentiment and wearable physiological signals to represent individual digital behavior. Layer 2 has <i>temporal graph neural network (TGNN)</i> with graph Laplacian Regularization for capturing intra- and inter-employee dependencies across tasks, tools, and interactions while preserving temporal and relational structures. Layer 3 contains <i>Self-Supervised Contrastive Learning Module</i> which leverages latent embeddings to automatically distill meaningful cross-employee patterns thus enhancing robustness against noise and distribution heterogeneity. Layer 4 is the <i>Fusion and Classification Layer</i> which integrates embeddings from all prior layers to generate risk scores for digital strain, overwork, and techno-stress. Experimental evaluation on multiple workplace datasets demonstrates that AutoEG achieves the highest digital strain accuracy (DSRS) thus outperforming baseline machine learning and graph neural network models.</p>

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AutoEG: a hybrid deep learning framework to detect dark side of digital work practices on employees in IT sector

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Garima Bhardwaj,
  • Vijayalakshmi Iyengar

摘要

Employee digital strain has emerged as a critical factor affecting workplace productivity and well-being especially in the modern fast paced work environment. Accurately detecting overwork, multitasking, and burnout requires modeling complex behavioral and relational dependencies from multimodal digital activity data. In this work, we propose AutoEG, an automated graph-based contrastive learning framework for robust employee stress representation and classification. Layer 1 comprises multimodal behavioral encoder (MBE) which extracts embeddings from keystrokes, application usage logs, and chat/email sentiment and wearable physiological signals to represent individual digital behavior. Layer 2 has temporal graph neural network (TGNN) with graph Laplacian Regularization for capturing intra- and inter-employee dependencies across tasks, tools, and interactions while preserving temporal and relational structures. Layer 3 contains Self-Supervised Contrastive Learning Module which leverages latent embeddings to automatically distill meaningful cross-employee patterns thus enhancing robustness against noise and distribution heterogeneity. Layer 4 is the Fusion and Classification Layer which integrates embeddings from all prior layers to generate risk scores for digital strain, overwork, and techno-stress. Experimental evaluation on multiple workplace datasets demonstrates that AutoEG achieves the highest digital strain accuracy (DSRS) thus outperforming baseline machine learning and graph neural network models.