ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
ValueGraph introduces a novel graph pre-training framework that incorporates automatically inferred moral-value signals as soft constraints for contextualized user representation learning The model learns from post-reply graphs, capturing both semantic and structural user representations while aligning users through relative value similarity using contrastive and clustering objectives Value signals are treated as noisy auxiliary signals rather than ground-truth psychological labels, enabling rob
Analysis
TL;DR
- ValueGraph introduces a novel graph pre-training framework that incorporates automatically inferred moral-value signals as soft constraints for contextualized user representation learning
- The model learns from post-reply graphs, capturing both semantic and structural user representations while aligning users through relative value similarity using contrastive and clustering objectives
- Value signals are treated as noisy auxiliary signals rather than ground-truth psychological labels, enabling robust representation learning despite signal imperfection
- Experiments on stance detection and Twitter bot detection demonstrate consistent improvements over strong text-based, graph-based, and text-only LLM baselines
- The work establishes value-signal guidance as a meaningful inductive bias for socially informed user modeling on social media platforms
Why It Matters
This research addresses a critical gap in user representation learning by incorporating value-relevant dimensions that existing methods overlook, offering a more holistic approach to understanding user behavior on social media. For AI practitioners building detection systems or recommendation models, integrating value signals as soft constraints provides a practical pathway to improve performance on socially grounded tasks without requiring expensive labeled data. The framework's emphasis on treating inferred values as noisy rather than gold labels also offers a principled approach to handling the inherent uncertainty in automated value inference.
Technical Details
- Architecture: ValueGraph is a graph pre-training framework built on post-reply graphs, learning joint semantic and structural representations of users through graph-based pre-training
- Value-Signal Integration: Automatically inferred moral-value signals serve as noisy auxiliary signals; the model aligns users through relative value similarity using both contrastive learning and clustering objectives, rather than treating values as hard labels
- Soft Constraint Design: The framework explicitly avoids treating inferred values as ground-truth psychological labels, instead using them as soft constraints that guide representation learning while tolerating signal noise
- Evaluation Benchmarks: Experiments conducted on two downstream tasks—stance detection and Twitter bot detection—comparing against text-based, graph-based, and text-only LLM baselines
- Domain: Computation and Language (cs.CL), Artificial Intelligence (cs.AI), and Machine Learning (cs.LG)
Industry Insight
- Social media platforms and content moderation teams can leverage value-signal-guided representation learning to improve detection of coordinated inauthentic behavior, bots, and polarized stance expression without relying solely on textual features
- The soft-constraint approach to noisy auxiliary signals offers a transferable paradigm for incorporating any inferred user-level attribute (e.g., ideological leaning, behavioral tendencies) into representation learning pipelines where ground-truth labels are unavailable or unreliable
- As AI-driven content analysis becomes increasingly regulated, methods that incorporate value-aware modeling with explicit uncertainty handling may face fewer ethical and compliance challenges compared to approaches treating inferred attributes as definitive labels
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