[Submitted on 30 Aug 2026]
Title:ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
View a PDF of the paper titled ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation, by Yitong Han and 5 other authors
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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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2609.00057 [cs.CL]
(or arXiv:2609.00057v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.00057
arXiv-issued DOI via DataCite
Submission history
From: Wei Gao [view email] [v1] Sun, 30 Aug 2026 13:39:30 UTC (4,405 KB)
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