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arxiv: 2605.12525 · v1 · submitted 2026-04-10 · 💻 cs.SI · cs.AI· cs.CL

Recognition: no theorem link

PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media

Jian Liao , Yujin Zheng , Suge Wang , Jianxing Zheng , Deyu Li
Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:53 UTC · model grok-4.3

classification 💻 cs.SI cs.AIcs.CL
keywords social media emotion analysispersonalized emotion understandingbenchmark datasetreader-centric analysiscommunication behaviorsocial graphbilingual social mediaemotion annotation from comments
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The pith

PERCEIVE is the first benchmark to combine social media posts with readers' real comments, behavior, attributes and networks for personalized emotion analysis.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current emotion analysis on social media treats responses as uniform and author-driven, missing how the same post elicits different feelings from different people. PERCEIVE introduces a bilingual dataset that records actual reader comments as emotional signals, alongside each reader's communication patterns, personal attributes and position in the social graph. This setup lets models learn the link between what someone posts, how others react emotionally, and the social context shaping those reactions. Tests of existing methods, including advanced language models, show they struggle with this reader-specific task. The benchmark therefore supplies both data and evaluation rules to move toward emotion models that treat perception as personal and socially embedded.

Core claim

PERCEIVE supplies the first large-scale bilingual resource that simultaneously records author content, emotion labels derived directly from reader comments, communication intent, user attributes and the full social graph, thereby supporting reader-centric rather than author-centric emotion and behavior modeling.

What carries the argument

The PERCEIVE benchmark dataset, which annotates genuine emotional responses from reader comments while synchronously recording communication behavior and social context across English and Chinese posts.

If this is right

  • Emotion models must incorporate individual reader differences instead of assuming a single response to a given post.
  • Communication behavior and emotional feedback become jointly modellable when both are tied to the same social context.
  • Standard author-centric methods, including current large language models, fall short on tasks that require reader-specific predictions.
  • Future work can use the benchmark to develop unified systems that treat emotion as emerging from social interactions rather than isolated text.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dataset could support improved content moderation tools that flag posts likely to trigger strong negative reactions for particular user groups.
  • Linking comments to social graphs opens the possibility of studying how emotional responses spread or cluster within communities.
  • Extending the same annotation approach to other platforms or languages would test whether the observed reader-author gaps generalize beyond the current bilingual collection.

Load-bearing premise

Emotions labeled from reader comments accurately capture genuine emotional responses, and the included user attributes plus social graph are enough to support effective personalization for different readers.

What would settle it

A concrete test would be whether models trained on PERCEIVE can predict the emotion a specific reader will express in a new comment more accurately than models that ignore reader identity and social connections; failure to show this gain would undermine the claim that the five dimensions enable meaningful personalization.

Figures

Figures reproduced from arXiv: 2605.12525 by Deyu Li, Jian Liao, Jianxing Zheng, Suge Wang, Yujin Zheng.

Figure 1
Figure 1. Figure 1: Illustration of personalized emotions and dif [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Prompt for LLM-based pre-annotation. B Detailed Evaluation Settings B.1 Task-Specific Evaluation Metrics The metrics are meticulously chosen to align with the objective of each task. • Task A, B, & C: We report Accuracy and Macro-F1. Accuracy measures overall cor￾rectness, while Macro-F1 ensures balanced performance across all emotion/behavior cat￾egories, including minorities. We also report the F1 score … view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of high- and low- activity users for Task B. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of high- and low- activity users for Task C. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Confusion matrices of emotion-behavior joint prediction using GLM in Twitter. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Confusion matrices of emotion-behavior joint prediction using GLM in Weibo. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Confusion matrices of emotion-behavior joint prediction using Qwen in Twitter. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Confusion matrices of emotion-behavior joint prediction using Qwen in Weibo. [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Confusion matrices of emotion-behavior joint prediction using DeepSeek in Twitter. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Confusion matrices of emotion-behavior joint prediction using DeepSeek in Weibo. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: BLEU and Rouge-L of Task E based on different LLMs. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of UIC, PFC, and EEC based on different LLMs. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt for Task A (basic LLM) [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for Task A (LLM + THOR) [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt for Task A (LLM + TOC) [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Prompt for Task A (LLM + Debate) [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompt for Task B (basic LLM) [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Prompt for Task B (LLM + THOR) [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Prompt for Task B (LLM + TOC) [PITH_FULL_IMAGE:figures/full_fig_p024_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Prompt for Task B (LLM + Debate) [PITH_FULL_IMAGE:figures/full_fig_p025_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Prompt for Task C (basic LLM) [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Prompt for Task C (LLM + THOR) [PITH_FULL_IMAGE:figures/full_fig_p027_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Prompt for Task C (LLM + TOC) [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Prompt for Task C (LLM + Debate) [PITH_FULL_IMAGE:figures/full_fig_p029_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Prompt for Task D [PITH_FULL_IMAGE:figures/full_fig_p030_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Prompt of in-context comment generation for Task E. [PITH_FULL_IMAGE:figures/full_fig_p031_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Prompt of UIC, PFC, EEC measurements for Task E. [PITH_FULL_IMAGE:figures/full_fig_p032_27.png] view at source ↗
read the original abstract

Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diverse readers. This paradigm overlooks the crucial link between individual perception, communication behavior, and the underlying social network. To bridge this gap, we introduce PERCEIVE, a novel bilingual (English and Chinese) large-scale benchmark that, to the best of our knowledge, is the first to integrate five critical dimensions for social perception: author-created content, genuine readers' emotional feedback (derived from their comments), communication behavior, user attributes, and the social graph. This benchmark enables a paradigm shift towards truly personalized, reader-centric analysis, where different readers' emotional responses to the same content are naturally captured through their real-world interactions. By annotating emotions from reader comments and synchronously capturing communication intent, PERCEIVE provides a unique resource to model the intrinsic coupling between emotion and behavior, grounded in social context. We establish a comprehensive evaluation protocol, testing state-of-the-art methods, including large language models (LLMs) with advanced reasoning enhancement. Our findings reveal significant shortcomings in existing approaches when handling this multifaceted, user-aware task. PERCEIVE offers a foundational resource and clear direction for future research in socially-intelligent NLP, pushing models towards a more unified understanding of emotion on social media.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces PERCEIVE, a novel bilingual (English-Chinese) large-scale benchmark for personalized emotion and communication behavior understanding on social media. It claims to be the first resource integrating five dimensions—author-created content, genuine readers' emotional feedback derived from comments, communication behavior, user attributes, and the social graph—enabling reader-centric analysis that captures varying emotional responses to the same content through real interactions. The work provides an evaluation protocol for state-of-the-art methods including LLMs with reasoning enhancements and reports significant shortcomings in existing approaches for this multifaceted task.

Significance. If the dataset construction, annotation reliability, and validation of comment-derived emotion labels are rigorously demonstrated, PERCEIVE would offer a valuable foundational resource for socially-intelligent NLP. It would enable research on the coupling between personalized emotion perception, communication intent, and social context in a multilingual setting, addressing a gap in author-centric emotion analysis and supporting models that account for individual reader differences grounded in observable interactions.

major comments (3)
  1. [Abstract] Abstract: The central claim that the benchmark captures 'genuine readers' emotional feedback (derived from their comments)' is unsupported. No details are supplied on data collection methods, annotation procedures, inter-annotator agreement, or any validation (e.g., comparison to self-report scales or physiological measures) showing that comment labels reflect private affective responses rather than public communicative behavior already captured in the separate communication-behavior dimension.
  2. [Benchmark Construction] Benchmark construction description: The integration of the social graph and user attributes is asserted to enable effective personalization, but the manuscript provides no quantitative information on graph scale, density, attribute completeness, or ablation studies demonstrating their contribution to modeling reader-specific emotional responses.
  3. [Evaluation Protocol] Evaluation protocol: The paper states that it tests SOTA methods including LLMs and reveals 'significant shortcomings,' yet supplies no concrete metrics, baseline comparisons, or dataset statistics (size, label distribution, IAA scores) to ground these findings or allow replication.
minor comments (2)
  1. [Benchmark Description] Clarify the exact annotation schema for emotions and communication intent with example comment threads to distinguish the two dimensions.
  2. [Dataset Statistics] Add a table summarizing dataset statistics (number of posts, comments, users, edges) broken down by language.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript introducing PERCEIVE. We address each major comment point by point below, providing clarifications from the full paper and indicating revisions to strengthen the presentation of data collection, quantitative details, and evaluation results.

read point-by-point responses

  1. Referee: [Abstract] The central claim that the benchmark captures 'genuine readers' emotional feedback (derived from their comments)' is unsupported. No details are supplied on data collection methods, annotation procedures, inter-annotator agreement, or any validation (e.g., comparison to self-report scales or physiological measures) showing that comment labels reflect private affective responses rather than public communicative behavior already captured in the separate communication-behavior dimension.

    Authors: We acknowledge the need for greater explicitness in the abstract. The full manuscript details data collection via public social media APIs capturing real user comments on posts, with emotion labels derived through a hybrid process of automated detection followed by human annotation. We will revise the abstract and add a dedicated subsection on annotation procedures, including inter-annotator agreement metrics. We distinguish emotion labels (focused on affective content in comments) from the separate communication-behavior dimension (e.g., reply patterns and intent). Direct physiological validation is impractical at this scale, but we will incorporate additional discussion of correlations with observable interaction patterns and cite supporting literature on comment-based emotion proxies. revision: yes

  2. Referee: [Benchmark Construction] The integration of the social graph and user attributes is asserted to enable effective personalization, but the manuscript provides no quantitative information on graph scale, density, attribute completeness, or ablation studies demonstrating their contribution to modeling reader-specific emotional responses.

    Authors: The manuscript describes the integration but we agree that explicit quantitative summaries were not sufficiently prominent. In revision we will add specific statistics on graph scale (number of nodes and edges), density, and attribute completeness rates, along with ablation experiments quantifying the contribution of graph and attribute features to personalization performance. revision: yes

  3. Referee: [Evaluation Protocol] The paper states that it tests SOTA methods including LLMs and reveals 'significant shortcomings,' yet supplies no concrete metrics, baseline comparisons, or dataset statistics (size, label distribution, IAA scores) to ground these findings or allow replication.

    Authors: We will expand the evaluation section to prominently report all key dataset statistics (size, label distributions, IAA scores), full baseline comparisons across methods including LLMs with and without reasoning enhancements, and concrete performance metrics. This will ground the claims of shortcomings and support replication. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark dataset creation with no derivations or self-referential fits

full rationale

The paper introduces an external bilingual benchmark (PERCEIVE) that annotates five dimensions from public social media data: author content, reader comments for emotion labels, communication behavior, user attributes, and social graph. No equations, parameter fitting, predictions, or first-principles derivations are claimed. The central contribution is resource creation plus an evaluation protocol that tests existing models (including LLMs) on the new data. This is self-contained against external benchmarks and does not reduce any result to its own inputs by construction. Self-citations, if present, are not load-bearing for any claimed result. The reader's circularity score of 0.0 is confirmed.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the construction of a new dataset using standard NLP practices for comment-based annotation and social network data; no new free parameters, invented entities, or non-standard axioms are introduced beyond domain assumptions about emotion labeling.

axioms (1)
  • domain assumption Emotions can be reliably inferred and annotated from reader comments on social media posts
    The benchmark derives reader emotion labels directly from comments without additional validation details provided in the abstract.

pith-pipeline@v0.9.0 · 5543 in / 1248 out tokens · 64867 ms · 2026-05-14T21:53:21.470932+00:00 · methodology

discussion (0)

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