ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation

Yitong Han; Wei Gao; Yi Zhao; Prasanta Bhattacharya; Fengzhu Zeng; Mohammad Amanlou

cs.CLAug 30, 2026

ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation

Authors: Yitong Han, Wei Gao, Yi Zhao, Prasanta Bhattacharya, Fengzhu Zeng, Mohammad Amanlou

Organizations: School of Computing and Information Systems, Singapore Management University, Singapore · Institute of Advanced Intelligence and Computing, A*STAR, Singapore

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.

Figures & tables

Figure 1: The framework of ValueGraph . In Stage 1, we construct a post-reply graph and perform GraphMAE2-based unsupervised pre-training to learn contextualized post representations by jointly modeling semantic and structural information. The resulting post embeddings are aggregated into user representations. In Stage 2, we introduce moral value signals extracted by MoralBERT and optimize user representations through value-guided contrastive learning and clustering objectives. The learned user representations are then used for downstream fine-tuning on stance detection and Twitter bot detection.

			LLM	Graph Pre-training		PLM			GNN			ValueGraph
Model	Dataset	Metric	GPT-5.4	GraphCL	GraphMAE2	SimCSE	ModernBERT	Bertweet	GCN	GAT	GTN
GLAN	MT_CSD	Acc.	0.58	0.41	0.60	0.60	0.56	0.58	0.40	0.42	0.57	0.63 ∗
GLAN	MT_CSD	MacF1	0.56	0.38	0.55	0.56	0.53	0.51	0.35	0.33	0.56	0.58 ${}^{~}$
BrLSTM	RumourEval19	Acc.	0.73	0.59	0.67	0.74	0.74	0.65	0.61	0.31	0.71	0.77 ∗
BrLSTM	RumourEval19	MacF1	0.47	0.38	0.47	0.53	0.59	0.56	0.25	0.18	0.45	0.71 ∗

Table 1: Stance detection results on MT_CSD and RumourEval19. ∗ indicates statistical significance at 99% confidence level compared with the second-best performance based on two-tailed paired Student’s

t

-test.

Method	Setting	Type	Accuracy	MacF1	Precision	Recall	MCC
GPT-5.4	Text-only	T	56.9 $(\pm 0.1)$	52.1 $(\pm 0.1)$	57.1 $(\pm 0.1)$	54.9 $(\pm 0.1)$	11.8 $(\pm 0.1)$
RoBERTa	Baseline	T	50.7 $(\pm 0.2)$	54.8 $(\pm 0.5)$	48.8 $(\pm 0.1)$	62.5 $(\pm 1.2)$	–
	ValueGraph	T	56.9 $(\pm 0.6)$ ∗ ∗	64.3 $(\pm 0.4)$ ∗ ∗	53.3 $(\pm 0.6)$ ∗ ∗	81.0 $(\pm 2.3)$ ∗	–
	Baseline	UT	63.1 $(\pm 0.5)$	66.2 $(\pm 0.5)$	58.9 $(\pm 0.5)$	75.6 $(\pm 1.4)$	–
	ValueGraph	UT	65.9 $(\pm 0.3)$ ∗ ∗	68.9 $(\pm 2.4)$ ∗	61.2 $(\pm 0.6)$	78.7 $(\pm 1.8)$ ∗	–
BotGCN (T5)	Baseline	TG	56.8 $(\pm 6.0)$	64.4 $(\pm 1.3)$	53.7 $(\pm 5.1)$	82.1 $(\pm 8.1)$	17.9 $(\pm 9.7)$

Table 2: Comparison of text-only GPT-5.4, baseline embeddings, and ValueGraph embeddings for twitter bot detection. * and ** indicate statistical significance at 95% and 99% confidence levels, respectively, based on two-tailed paired Student’s

t

-test between ValueGraph and the corresponding baseline embedding.

Panel of Figure 2 — Figure 2: t-SNE visualization of user embeddings for Twitter bot detection. Red and grey dots represent ground-truth human and bot users, respectively.

Task	Model/data	Configuration	Accuracy	MacF1
Stance	GLAN (MT_CSD)	Only Stage 1	41.2	40.7
		Stage 1 + $\mathcal{L}_{\text{cls}}$	50.4	34.3
		Stage 1 + $\mathcal{L}_{\text{user}}$	45.3	32.6
		Full	63.0	58.0
	BrLSTM (RumourEval19)	Only Stage 1	53.7	55.8
		Stage 1 + $\mathcal{L}_{\text{cls}}$	62.7	52.8

Table 3: Ablation results of ValueGraph embeddings under different training settings across stance detection and Twitter bot detection tasks. For Twitter bot detection, BotGAT is evaluated in the FTUG setting.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 3: Distribution of RBF-based similarities for 100,000 randomly sampled user pairs.

Panel of Figure 4 — Figure 4: Hyperparameter configurations used in ValueGraph training. All settings use temperature $\tau=0.07$ , margin coefficient $\beta=0.5$ , margin $m=1$ , and clustering loss weight $\lambda_{\mathrm{cluster}}=0.05$ . The three configurations vary in the number of augmented users $k$ and the number of clusters $K$ .

Subreddit	Comments
Politics	5,626,191
Neoliberal	2,315,326
Relationship Advice	861,704
Confession	477,353
Conservative	165,182
Worldnews	100,194

Appendix

Table 4: Statistics of the Reddit pre-training corpus.

Dataset	Posts
BEARD ( Zeng and Gao 2022 )	3,393,578
Twitter-Covid ( Lin et al. 2022 )	474,710
PHEME ( Zubiaga et al. 2016 )	59,387
Twitter16 ( Ma et al. 2016 )	1,101,985
Total	5,029,660

Appendix

Table 5: Statistics of the Twitter pre-training corpus.

Split	Label	Count
Train	Against	3,349
	Favor	2,383
	None	5,441
Valid	Against	710
	Favor	481
	None	1,115

Appendix

Table 6: Statistics for MT_CSD dataset

Split	Label	Count
Train	Comment	2,734
	Deny	333
	Query	330
	Support	841
Dev	Comment	173
	Deny	11

Appendix

Table 7: Statistics for RumourEval19

U^{+}_{\mathrm{seed}}\leftarrow\bigcup_{u\in U_{\mathrm{seed}}}\left(\tilde{u}\overset{k}{\sim}\mathcal{S}(u)\cup\hat{u}\overset{k}{\sim}\mathcal{D}(u)\right)

Appendix

Algorithm 1 ValueGraph Training

Model	Hidden Dim	Learning Rate	Weight Decay	Dropout	Batch Size	Max Epoch
GLAN (MT_CSD)	512	1e-5	1e-4	0.2	32	100
BrLSTM (RumourEval19)	300	1e-4	1e-4	0.5	16	100

Appendix

Table 8: Hyperparameter settings for stance detection models.

Model	Hidden Dim	Learning Rate	Weight Decay	Dropout	Batch Size	Max Epoch
RoBERTa (T)	128	1e-4	1e-5	0.5	96	60
RoBERTa (T,U)	128	1e-4	1e-5	0.5	96	60
BotRGCN (T,G)	256	5e-4	5e-5	0.5	128	300
BotRGCN (F,T,U,G)	128	5e-4	1e-3	0.3	128	300
GAT (T,G)	96	2e-4	5e-5	0.5	128	300
GAT (F,T,U,G)	96	2e-4	5e-5	0.5	256	300

Appendix

Table 9: Hyperparameter settings for Twitter bot detection models.

Task	Model/dataset	Configuration	Accuracy	F1-score
Stance Detection	GLAN (MT_CSD)	Only Stage 1	41.2	40.7
		Stage 1 + $\mathcal{L}_{\text{cls}}$	50.4	34.3
		Stage 1 + $\mathcal{L}_{\text{user}}$	45.3	32.6
		Full	63.0	58.0
	BrLSTM (RumourEval19)	Only Stage 1	53.7	55.8
		Stage 1 + $\mathcal{L}_{\text{cls}}$	62.7	52.8

Appendix

Table 10: Ablation results under different training configurations across two tasks: stance detection and Twitter bot detection. For Twitter bot detection, RoBERTa is evaluated in the UT setting, while BotGAT, BotGCN, and BotRGCN are evaluated in the FTUG setting.

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