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REVIEW 4 major objections 7 minor 66 references

Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A dual-aspect empathy framework that models both the creator's tactics and the reader's emotional reactions outperforms eight baselines on two misinformation benchmarks.

desk verdict The reported gains are plausible but unproven: the synthetic reader comments may be leaking the veracity label, and the paper never isolates them from real comments. read the letter →

arxiv 2504.17332 v1 pith:WIPBCXSZ submitted 2025-04-24 cs.CL

Zihan Wang , Lu Yuan , Zhengxuan Zhang , Qing Zhao This is my paper · ORCID
classification cs.CL
keywords misinformationdetectionmultimodalempathylargelanguagemodelsfakenewscommentsimulationcognitiveemotional
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a misinformation detector becomes more accurate when it models empathy in both directions: the creator's cognitive strategies and emotional appeals, and the reader's cognitive judgment and emotional response. To supply the reader side, the paper uses GPT-4o to simulate comments from people of different ages, genders, and education levels, then filters these comments for empathy signals and selects the top five per news item. Creator and reader representations are fused with the text and image content, and the absolute difference between their emotional signals—the emotion gap—is added as a feature. On the PHEME and PolitiFact benchmarks, the resulting Dual-Aspect Empathy (DAE) model reaches 89.8% and 90.6% accuracy, respectively, ahead of the eight baselines compared on each dataset. The claim matters because, if correct, it shows that psychological empathy signals, not just surface content, carry useful information for detecting misinformation.

What carries the argument

The central object is the dual-aspect empathy vector pair built from creator and reader features. The creator's empathy vector is the mean-pooled encoding of text and image; the reader's vector is the max-pooled encoding of the top five LLM-simulated comments. The emotion gap, $e_{\text{gap}} = |e_{\text{creator}} - e_{\text{reader}}|$, is the difference between the two and is concatenated with both vectors to form the emotional empathy representation; a parallel cognitive representation fuses creator, reader, and cross-modal text-image features. This vector construction carries the argument by making the model explicitly compare what the creator emits with what readers take away.

What would settle it

Compare DAE trained on GPT-4o-generated comments with DAE trained on real reader comments matched for demographic profile, while holding the rest of the model fixed. If the accuracy gap is small, the empathy features generalize; if the simulated-comment version is much more accurate, the reported gains likely come from simulation artifacts or label leakage rather than from modeling empathy.

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Extended reading notes

Core claim

DAE treats misinformation detection as a two-sided empathy problem. On the creator side, mean-pooled text and image features represent communicative intent; on the reader side, GPT-4o generates comments from simulated demographic profiles, an empathy-aware filter keeps comments with cognitive or emotional content, and a pointer network selects the top five. The model computes both a cognitive fusion of creator, reader, and cross-modal text-image features and an emotional fusion that includes the absolute creator-reader emotion gap, concatenates them, and classifies with an MLP. The reported results are 89.8% accuracy on PHEME and 90.6% on PolitiFact, ahead of all eight baselines per dataset; ablations attribute the largest drop to removing tweet text, followed by removing comments, and show that emotional empathy helps more on PolitiFact while cognitive empathy helps more on PHEME.

Load-bearing premise

The paper assumes that GPT-4o's simulated demographic comments are authentic, diverse, and independent of the true label, so the empathy signals they carry come from reader psychology rather than from artifacts of the simulation or the filtering process.

Editorial extensions

If this is right

  • Using LLM-simulated reader comments, filtered for empathy and selected by top-$k$, can improve multimodal misinformation detection beyond content-only baselines.
  • The emotion gap between creator and reader emotional signals is a usable feature: removing it lowers accuracy on both datasets (PHEME from 89.84 to 87.34, PolitiFact from 90.57 to 84.91).
  • Cognitive and emotional empathy are complementary and dataset-dependent: on PolitiFact, emotional empathy matters more, while on PHEME cognitive empathy matters more.
  • Comment quantity matters: selecting five comments is optimal, with performance degrading at one to two comments and at seven to eight comments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: replacing GPT-4o-simulated comments with real reader comments matched for demographic profile would show whether the empathy features themselves generalize or whether the gain depends on the simulated source.
  • Editorial inference: if the filtering step preferentially keeps comments whose sentiment aligns with the ground-truth label, the emotion gap may be encoding label leakage; this could be checked by training a model that uses only comment sentiment polarity as features.
  • Editorial inference: the framework could be ported to smaller open-source LLMs; whether the accuracy gain survives a less powerful simulation model would indicate whether the value lies in the empathy architecture or in the scale of the simulator.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. DAE is a binary multimodal misinformation detector that combines creator-side text and image encodings with reader-side comment features. The reader signal consists of up to 15 real comments plus GPT-4o-generated comments from demographic profiles, filtered qualitatively by an 'empathy-driven' criterion, embedded with RoBERTa, and selected by a top-k mechanism. An emotion gap between mean-pooled creator features and max-pooled reader features is concatenated with a cognitive fusion term and classified by an MLP. On PHEME and PolitiFact the paper reports 89.8% and 90.6% accuracy, outperforming eight baselines on each dataset, with ablations showing that text, comments, selection, and the two empathy aspects each contribute.

Significance. The research question is worthwhile: modeling reader empathy via LLM simulation is a plausible route, and the DAE architecture is coherent. Credit is due for the clear task definition, the broad baseline suite, the ablation study, the case study, and the parameter analysis; if the reported results were reproduced under contamination controls, the framework would be a solid advance for multimodal misinformation detection. However, the main empirical claim currently rests on a potentially leaky data-generation channel: GPT-4o may encode outcome knowledge of the benchmark events, and the paper does not isolate the synthetic comments from real ones. Without a label-correlation audit or a real-comments-only control, the gains in Table 2 cannot be attributed to empathy modeling. The missing specification of the Pointer Network and the test-set selection of k further weaken the evidence. For these reasons the paper is not yet acceptable in its present form.

major comments (4)
  1. [3.2.1, Eq. (2), Fig. 4] The simulated comments are generated by GPT-4o with the full news text, image, and existing comments as input, and the cognitive-empathy instruction explicitly asks the model to 'evaluate credibility based on prior knowledge.' Since PHEME and PolitiFact concern events (Charlie Hebdo, Sydney siege, etc.) whose outcomes are part of GPT-4o's pretraining, the generated comments such as 'I have serious reservations about the narrative' in Fig. 4 can encode posterior knowledge of the label rather than a demographic reader's live reaction. The filtering in Section 3.2.2 is described only qualitatively and may preferentially retain such skeptical comments. The w/o comments ablation removes both real and synthetic comments and therefore cannot isolate whether the Table 2 gains come from the synthetic source. I request: (i) a DAE variant trained with only real comments; (ii) a correlation analysis between generated-comment features (e.g., sentiment or expressed skepticism) and ground-truth labels; (iii) an evaluation on event data after GPT-4o's knowledge cutoff or with a non-factual baseline generator.
  2. [4.1, Eq. (13)] The Pointer Network that 'selects the top-k comments' is mentioned only in the setup paragraph; no architecture, input representation, training objective, or inference procedure is given anywhere in the paper. Eq. (13) is a simple top-k operator. Since the selection mechanism is one of the paper's stated contributions and one of the ablation components (w/o select), its unspecified design prevents reproduction and meaningful interpretation of the ablation. Please provide a full specification or remove the reference to a Pointer Network.
  3. [Table 2, Section 4.4] All results are single runs with no error bars, standard deviations, or significance tests. The reported margins over the strongest baselines are modest on several key metrics (e.g., 1.4 accuracy points over BMR on PHEME, 1.6 F1 points over QMFND on PolitiFact). Without multiple seeds or statistical testing, the word 'significantly' used in Section 4.3 is unsupported. Please report seeded repetitions and, if possible, confidence intervals or paired significance tests.
  4. [4.5] The parameter analysis sweeps k on the same test sets and reports the best test accuracy at k=5. This is test-set tuning, which inflates expected performance; the reported main results should use a k chosen on a validation split, with the test set reserved for final evaluation. The same concern applies to the unvaried thresholds fixed in Sections 3.2.2 and 4.1 (minimum five comments per article, maximum fifteen real comments), which are free hyperparameters that should be justified or validated.
minor comments (7)
  1. [1 (Contributions)] In the third contribution bullet, 'a innovative' should be 'an innovative.'
  2. [3.2.2] The statement 'these were translated' is unclear; specify the translation method, the source languages, and whether translations were reviewed.
  3. [4.1, Eq. (20)] Training details are incomplete: number of epochs, batch size, learning-rate schedule, and warm-up are not given, and label smoothing is mentioned without a smoothing coefficient.
  4. [4.4] The definitions of w/o emotion and w/o cognition are ambiguous; state precisely which components are removed (e.g., whether e_gap is dropped or h_e reduces to e_reader only).
  5. [Fig. 4] The check/cross symbols and the 'Ours w/o ...' row layout are not explained in the caption; clarify what is being marked.
  6. [References] The dataset citations appear incorrect: PHEME is cited to Zhang et al. [54] (which is actually the LNI paper) and PolitiFact is cited to Jin et al. [14]; please cite the original PHEME dataset paper and the PolitiFact corpus.
  7. [2.1] The related work mentions [26] Nan et al. on LLM-generated comments; the present method is close to that line of work, and the comparison should explicitly state the novelty beyond [26] (e.g., demographic simulation and filtering).

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity; DAE's claimed gains are an empirical accuracy claim from standard supervised learning, not a derivation that reduces to its own inputs.

full rationale

The paper claims an empirical accuracy improvement (89.8% on PHEME and 90.6% on PolitiFact, Section 4.3) from a supervised classifier over text, images, real comments, and GPT-4o-simulated comments. Equations (1)-(20) define features—mean/max pooling, multi-head self-attention, cross-modal attention, and the emotion gap—directly from the multimodal inputs and comments; none of these definitions presupposes the ground-truth label or the reported accuracy. No fitted parameter is renamed as a prediction: the top-k selection is an empirically tuned hyperparameter, and the parameter analysis reports performance on the test set, which is a methodological weakness but not a circular derivation. The only self-citations ([23] and [52], both involving co-author Zhengxuan Zhang) are background citations for social-media prevalence and misinformation spread; they are not load-bearing for the method or its claimed result. The GPT-4o comment simulation (Eq. 2) could in principle leak veracity if the LLM memorized the benchmark events, and the 'w/o comments' ablation does not isolate that channel, but this is a data-contamination/correctness risk, not circularity: the model's prediction is not defined in terms of the simulated comments, and the comments are not constructed from the labels. Since the central claim is an externally falsifiable benchmark comparison, the derivation chain is self-contained. Honest finding: no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on assumptions about LLM simulation fidelity and feature operationalization rather than on a formal derivation. The only data-fitted knob explicitly tuned to test performance is k=5. No new physical or conceptual entities are introduced.

free parameters (3)
  • Top-k comment selection quantity k = 5
    Selected from the parameter sweep in Section 4.5 based on test-set accuracy on both PHEME and PolitiFact; the final model uses k=5.
  • Minimum retained comments per article = 5
    Set in Section 3.2.2 to ensure contextual richness; no sensitivity analysis or justification beyond intuition.
  • Maximum real comments per news item = 15
    Set in Section 4.1; comments beyond this limit are truncated or replaced by generated ones, with no sensitivity analysis.
assumptions (4)
  • domain assumption GPT-4o simulations of demographic profiles faithfully reflect reader cognitive and emotional empathy.
    Section 3.2.1 assumes the LLM embodies different readers without human evaluation or comparison with real reader psychology.
  • domain assumption The top-k selection mechanism identifies the most informative comments.
    Section 3.4 defines top-k selection without specifying how the Pointer Network is trained or what supervision it uses.
  • domain assumption PHEME and PolitiFact preprocessing and the 8:2 split are valid for multimodal misinformation detection.
    Section 4.1 provides no code or data release, and the reported class counts differ from commonly used benchmark versions.
  • domain assumption Mean and max pooling with an absolute difference of embeddings operationalize cognitive and emotional empathy.
    Section 3.5 provides no theoretical or empirical support that these operations capture empathy rather than generic sentiment or importance.

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Cite this review

Pith. "Pith review of Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection." pith.science (2026). https://pith.science/paper/WIPBCXSZ

@misc{pith2026250417332,
  author       = {Pith},
  title        = {Pith review of: Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WIPBCXSZ}},
  note         = {Machine review of arXiv:2504.17332}
}
read the original abstract

In the digital era, social media has become a major conduit for information dissemination, yet it also facilitates the rapid spread of misinformation. Traditional misinformation detection methods primarily focus on surface-level features, overlooking the crucial roles of human empathy in the propagation process. To address this gap, we propose the Dual-Aspect Empathy Framework (DAE), which integrates cognitive and emotional empathy to analyze misinformation from both the creator and reader perspectives. By examining creators' cognitive strategies and emotional appeals, as well as simulating readers' cognitive judgments and emotional responses using Large Language Models (LLMs), DAE offers a more comprehensive and human-centric approach to misinformation detection. Moreover, we further introduce an empathy-aware filtering mechanism to enhance response authenticity and diversity. Experimental results on benchmark datasets demonstrate that DAE outperforms existing methods, providing a novel paradigm for multimodal misinformation detection.

Figures

Figures reproduced from arXiv: 2504.17332 by the authors.

Figure 1
Figure 1. An Example of Misinformation. with both the content and the creator, it becomes much easier to identify this as misinformation. This phenomenon inspires us to draw from the theory of em￾pathy in psychology to reconstruct the theoretical framework for misinformation detection. Empathy, as a core human ability to understand others’ psychological states, encompasses two com￾plementary dimensions: cognitive empathy and … view at source ↗
Figure 2
Figure 2. Overview of Our Proposed Dual-Aspect Empathy Framework (DAE) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Results of Ablation Study of 15 real comments and introduce GPT-generated supplementary comments. For text encoding, we use the pre-trained RoBERTa-base model, while visual encoding employs the pre-trained Swin-base model, freezing the first 𝑛 − 1 layers of the Swin Transformer and only training the last layer. The multi-head self-attention network uses 8 attention heads. Image features are extracted through the Swi… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Several Cases in the Test Set of Datasets [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Results of Parameter Analysis 87.74%). Conversely, on PHEME, cognitive empathy has a greater effect (from 89.84% to 87.17%) than emotional empathy (from 89.84% to 87.34%). These results highlight the necessity of tweet text, reader com￾ments, comment selection, and the…

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Works this paper leans on

66 extracted references · 43 canonical work pages

  1. [1]

    Sara Abdali, Sina Shaham, and Bhaskar Krishnamachari. 2024. Multi-modal misinformation detection: Approaches, challenges and opportunities. Comput. Surveys 57, 3 (2024), 1–29

    2024

  2. [2]

    Firoj Alam, Morena Danieli, and Giuseppe Riccardi. 2018. Annotating and mod- eling empathy in spoken conversations. Computer Speech & Language 50 (2018), 40–61

    work page 2018

  3. [3]

    Jawaher Alghamdi, Suhuai Luo, and Yuqing Lin. 2024. A comprehensive survey on machine learning approaches for fake news detection. Multimedia Tools and Applications 83, 17 (2024), 51009–51067

    work page 2024

  4. [4]

    Mohammad Q Alnabhan and Paula Branco. 2024. Fake news detection using deep learning: A systematic literature review. IEEE Access (2024)

    work page 2024

  5. [5]

    Miguel A Alonso, David Vilares, Carlos Gómez-Rodríguez, and Jesús Vilares

    work page

  6. [6]

    Vian Bakir and Andrew McStay. 2018. Fake news and the economy of emotions: Problems, causes, solutions. Digital journalism 6, 2 (2018), 154–175

    work page 2018

  7. [7]

    Waqas Haider Bangyal, Rukhma Qasim, Najeeb Ur Rehman, Zeeshan Ahmad, Hafsa Dar, Laiqa Rukhsar, Zahra Aman, and Jamil Ahmad. 2021. Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches. Computational and mathematical methods in medicine 2021, 1 (2021), 5514220

    work page 2021

  8. [8]

    Elena Broda and Jesper Strömbäck. 2024. Misinformation, disinformation, and fake news: lessons from an interdisciplinary, systematic literature review.Annals of the International Communication Association 48, 2 (2024), 139–166

    work page 2024

Show all 66 references
  1. [9]

    Sijing Chen, Lu Xiao, and Akit Kumar. 2023. Spread of misinformation on social media: What contributes to it and how to combat it.Computers in Human Behavior 141 (2023), 107643

    2023

  2. [10]

    Yixuan Chen, Dongsheng Li, Peng Zhang, Jie Sui, Qin Lv, Lu Tun, and Li Shang

  3. [11]

    Yaqian Dun, Kefei Tu, Chen Chen, Chunyan Hou, and Xiaojie Yuan. 2021. Kan: Knowledge-aware attention network for fake news detection. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 81–89

    2021

  4. [12]

    Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, and Zhiwen Yu. 2019. The fu- ture of misinformation detection: new perspectives and trends. arXiv preprint arXiv:1909.03654 (2019)

    2019 arXiv

  5. [13]

    Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi. 2024. Bad actor, good advisor: Exploring the role of large language models in fake news detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 22105–22113

    2024

  6. [14]

    Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, and Xing Xie. 2022. Towards fine-grained reasoning for fake news detection. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 5746–5754

    2022

  7. [15]

    Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on Multimedia . 795–816

    2017

  8. [16]

    Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. Mvae: Multimodal variational autoencoder for fake news detection. In The world wide web conference. 2915–2921

    2019

  9. [17]

    Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, and Manuel Gomez-Rodriguez. 2018. Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In Proceedings of the eleventh ACM international conference on web search and data mining . 324–332

    2018

  10. [18]

    Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, and Sowon Hahn. 2023. Chain of empathy: Enhancing empathetic response of large language models based on psychotherapy models. arXiv preprint arXiv:2311.04915 (2023)

    2023 arXiv

  11. [19]

    Yoon Kyung Lee, Jina Suh, Hongli Zhan, Junyi Jessy Li, and Desmond C Ong

  12. [20]

    Zhiwei Liu, Tianlin Zhang, Kailai Yang, Paul Thompson, Zeping Yu, and Sophia Ananiadou. 2024. Emotion detection for misinformation: A review. Information Fusion 107 (2024), 102300

    2024

  13. [21]

    Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recur- rent neural networks. (2016)

    2016

  14. [22]

    Weicheng Ma, Chunyuan Deng, Aram Moossavi, Lili Wang, Soroush Vosoughi, and Diyi Yang. 2024. Simulated misinformation susceptibility (smists): Enhancing misinformation research with large language model simulations. In Findings of the Association for Computational Linguistics ...

    2024

  15. [23]

    Weixing Mai, Zhengxuan Zhang, Kuntao Li, Yun Xue, and Fenghuan Li. 2023. Dynamic graph construction framework for multimodal named entity recognition in social media. IEEE Transactions on Computational Social Systems 11, 2 (2023), 2513–2522

    2023

  16. [24]

    Cameron Martel, Gordon Pennycook, and David G Rand. 2020. Reliance on emo- tion promotes belief in fake news. Cognitive research: principles and implications 5 (2020), 1–20

    2020

  17. [25]

    Muhammad Firoz Mridha, Ashfia Jannat Keya, Md Abdul Hamid, Muham- mad Mostafa Monowar, and Md Saifur Rahman. 2021. A comprehensive review on fake news detection with deep learning. IEEE access 9 (2021), 156151–156170

    2021

  18. [26]

    Qiong Nan, Qiang Sheng, Juan Cao, Beizhe Hu, Danding Wang, and Jintao Li. 2024. Let silence speak: Enhancing fake news detection with generated comments from large language models. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management ...

    2024

  19. [27]

    Anat Perry. 2023. AI will never convey the essence of human empathy. Nature Human Behaviour 7, 11 (2023), 1808–1809

    2023

  20. [28]

    Peng Qi, Zehong Yan, Wynne Hsu, and Mong Li Lee. 2024. Sniffer: Multimodal large language model for explainable out-of-context misinformation detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 13052–13062

    2024

  21. [29]

    Yushan Qian, Wei-Nan Zhang, and Ting Liu. 2023. Harnessing the power of large language models for empathetic response generation: Empirical investigations and improvements. arXiv preprint arXiv:2310.05140 (2023)

    2023 arXiv

  22. [30]

    Zhiguo Qu, Yunyi Meng, Ghulam Muhammad, and Prayag Tiwari. 2024. QMFND: A quantum multimodal fusion-based fake news detection model for social media. Information Fusion 104 (2024), 102172

    2024

  23. [31]

    Katherine P Rankin, Joel H Kramer, and Bruce L Miller. 2005. Patterns of cognitive and emotional empathy in frontotemporal lobar degeneration. Cognitive and Behavioral Neurology 18, 1 (2005), 28–36

    2005

  24. [32]

    Harita Reddy, Namratha Raj, Manali Gala, and Annappa Basava. 2020. Text- mining-based fake news detection using ensemble methods. International journal of automation and computing 17, 2 (2020), 210–221

    2020

  25. [33]

    Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 797–806

    2017

  26. [34]

    Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter 19, 1 (2017), 22–36

    2017

  27. [35]

    Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu. 2019. The role of user profiles for fake news detection. In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining . 436– 439

    2019

  28. [36]

    Shivangi Singhal, Anubha Kabra, Mohit Sharma, Rajiv Ratn Shah, Tanmoy Chakraborty, and Ponnurangam Kumaraguru. 2020. Spotfake+: A multimodal framework for fake news detection via transfer learning (student abstract). In Proceedings of the AAAI conference on artificial intellig...

    2020

  29. [37]

    Shivangi Singhal, Tanisha Pandey, Saksham Mrig, Rajiv Ratn Shah, and Ponnu- rangam Kumaraguru. 2022. Leveraging intra and inter modality relationship for multimodal fake news detection. In Companion Proceedings of the Web Conference

    2022

  30. [38]

    Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Ku- maraguru, and Shin’ichi Satoh. 2019. Spotfake: A multi-modal framework for fake news detection. In 2019 IEEE fifth international conference on multimedia big data (BigMM). IEEE, 39–47

    2019

  31. [39]

    Adam Smith. 2006. Cognitive empathy and emotional empathy in human behavior and evolution. The Psychological Record 56, 1 (2006), 3–21

    2006

  32. [40]

    Vera Sorin, Dana Brin, Yiftach Barash, Eli Konen, Alexander Charney, Girish Nadkarni, and Eyal Klang. 2024. Large Language Models and Empathy: Systematic Review. Journal of Medical Internet Research 26 (2024), e52597

    2024

  33. [41]

    Martin Steinebach, Karol Gotkowski, and Hujian Liu. 2019. Fake news detection by image montage recognition. In Proceedings of the 14th international conference on availability, reliability and security . 1–9

    2019

  34. [42]

    Qi Su, Mingyu Wan, Xiaoqian Liu, and Chu-Ren Huang. 2020. Motivations, methods and metrics of misinformation detection: an NLP perspective. Natural Language Processing Research 1, 1 (2020), 1–13

    2020

  35. [43]

    Mengzhu Sun, Xi Zhang, Jianqiang Ma, Sihong Xie, Yazheng Liu, and Philip S Yu. 2023. Inconsistent matters: A knowledge-guided dual-consistency network for multi-modal rumor detection. IEEE Transactions on Knowledge and Data Engineering 35, 12 (2023), 12736–12749

    2023

  36. [44]

    Michail Tsikerdekis and Sherali Zeadally. 2023. Misinformation Detection Using Deep Learning. IT Professional 25, 5 (2023), 57–63. doi:10.1109/MITP.2023.3314752

    2023

  37. [45]

    Herun Wan, Shangbin Feng, Zhaoxuan Tan, Heng Wang, Yulia Tsvetkov, and Minnan Luo. 2024. Dell: Generating reactions and explanations for llm-based misinformation detection. arXiv preprint arXiv:2402.10426 (2024)

    2024 arXiv

  38. [46]

    Bing Wang, Ximing Li, Changchun Li, Bo Fu, Songwen Pei, and Shengsheng Wang

  39. [47]

    Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng, Chenxu Yang, Weiping Wang, and Jie Zhou. 2022. Empathetic dialogue generation via sensitive emotion recognition and sensible knowledge selection. arXiv preprint arXiv:2210.11715 (2022)

    2022 arXiv

  40. [48]

    Lianwei Wu, Pusheng Liu, Yongqiang Zhao, Peng Wang, and Yangning Zhang

  41. [49]

    Yang Wu, Pengwei Zhan, Yunjian Zhang, Liming Wang, and Zhen Xu. 2021. Mul- timodal fusion with co-attention networks for fake news detection. In Findings of the association for computational linguistics: ACL-IJCNLP 2021 . 2560–2569

    2021

  42. [50]

    In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

    Why Misinformation is Created? Detecting them by Integrating Intent Features. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2304–2314

  43. [51]

    Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019)

    2019

  44. [52]

    WU Yin, Zhengxuan Zhang, WANG Fuling, Yuyu Luo, Hui Xiong, and Nan Tang. [n. d.]. Detecting Out-of-Context Misinformation via Multi-Agent and Multi-Grained Retrieval. ([n. d.])

  45. [53]

    Qichao Ying, Xiaoxiao Hu, Yangming Zhou, Zhenxing Qian, Dan Zeng, and Shiming Ge. 2023. Bootstrapping multi-view representations for fake news detection. In Proceedings of the AAAI conference on Artificial Intelligence , Vol. 37. 5384–5392

    2023

  46. [54]

    Qiang Zhang, Jiawei Liu, Fanrui Zhang, Jingyi Xie, and Zheng-Jun Zha. 2024. Natural language-centered inference network for multi-modal fake news detec- tion. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24. 2542–2550

    2024

  47. [55]

    Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, and Bo Wang. 2024. Rein- forcement tuning for detecting stances and debunking rumors jointly with large language models. arXiv preprint arXiv:2406.02143 (2024)

    2024 arXiv

  48. [56]

    Xinyi Zhou and Reza Zafarani. 2019. Fake news detection: An interdisciplinary research. In Companion proceedings of the 2019 world wide web conference . 1292– 1292

    2019

  49. [57]

    Xinyi Zhou and Reza Zafarani. 2020. A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR) 53, 5 (2020), 1–40

    2020

  50. [58]

    Jiahao Zhu, Zijian Jiang, Boyu Zhou, Jionglong Su, Jiaming Zhang, and Zhihao Li

  51. [59]

    Fabiana Zollo, Petra Kralj Novak, Michela Del Vicario, Alessandro Bessi, Igor Mozetič, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. 2015. Emo- tional dynamics in the age of misinformation. PloS one 10, 9 (2015), e0138740

    2015

  52. [60]

    Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020. : Similarity-aware multi-modal fake news detection. In Pacific-Asia Conference on knowledge discovery and data mining. Springer, 354–367

    2020

  53. [64]

    In Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

    Empathizing Before Generation: A Double-Layered Framework for Emo- tional Support LLM. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, 490–503

  54. [66]

    Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2017. Exploiting context for ru- mour detection in social media. In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part I 9 . Springer, 109–123. 8

    2017

  55. [2021]

    Electronics 10, 11 (2021), 1348

    Sentiment analysis for fake news detection. Electronics 10, 11 (2021), 1348

    2021

  56. [2022]

    In Proceedings of the ACM web conference 2022

    Cross-modal ambiguity learning for multimodal fake news detection. In Proceedings of the ACM web conference 2022 . 2897–2905

    2022

  57. [2023]

    IEEE Transactions on Knowledge and Data Engineering 36, 1 (2023), 211–225

    Human cognition-based consistency inference networks for multi-modal fake news detection. IEEE Transactions on Knowledge and Data Engineering 36, 1 (2023), 211–225. 7

    2023

  58. [2024]

    arXiv preprint arXiv:2403.18148 (2024)

    Large language models produce responses perceived to be empathic. arXiv preprint arXiv:2403.18148 (2024)

    2024 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.