ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
cs.CL, cs.AI, cs.LG
Submitted: 2026-08-30
Updated: 2026-08-30
Code: https://github.com/HanYiton/ValueGraph
License: http://creativecommons.org/licenses/by/4.0/
The gist: Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse.
Terminology
Abstract
Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse. User behavior on social media is shaped not only by what users say or whom they interact with, but also by the value signal through which they express attitudes. Existing user representation methods largely miss this value-relevant dimension. We propose ValueGraph, a graph pre-training framework that uses automatically inferred moral-value signals as noisy auxiliary signals for contextualized user representation. From post-reply graphs, ValueGraph learns semantic and structural representations and further aligns users through relative value similarity with contrastive and clustering objectives. Rather than treating inferred values as gold psychological labels, ValueGraph uses them as soft constraints for representation learning. Experiments on stance detection and twitter bot detection show consistent gains over strong text-based, graph-based, and text-only LLM baselines, highlighting value-signal guidance as a useful inductive bias for socially informed user modeling.
Sources
- GraphCL: Contrastive Self-Supervised Learning of Graph Representations
- Semi-Supervised Classification with Graph Convolutional Networks
- BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- The Moral Foundations Reddit Corpus
- Jointly embedding the local and global relations of heterogeneous graph for rumor detection
- A Challenge Dataset and Effective Models for Conversational Stance Detection
- Deep Graph Contrastive Representation Learning
- Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
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