Pith. sign in

REVIEW 4 major objections 7 minor 1 cited by

VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A single multimodal prompt lets a tuned LLM detect fake short videos at 90.9% accuracy.

desk verdict A plausible multimodal pipeline for short-video misinformation detection whose headline numbers are undermined by missing reproducibility and a very real label-memorization risk. read the letter →

arxiv 2411.10032 v1 pith:U4LRF4Q3 submitted 2024-11-15 cs.CV cs.AI

Weihao Zhong , Yinhao Xiao , Minghui Xu , Xiuzhen Cheng This is my paper · ORCID
classification cs.CVcs.AI
keywords multimodalfusionshortvideomisinformationlargelanguagemodelLoRAfine-tuningFakeSVdatasetnewsdetectionaudiotranscriptionvisual-language
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

The paper claims that misinformation in short videos can be detected reliably by converting every modality—subtitles, speech, visual content, and metadata—into one text prompt and feeding it to a large language model fine-tuned with LoRA. On the FakeSV benchmark this VMID framework reaches 90.93% accuracy and 90.89% macro F1, beating the strongest baseline by roughly ten percentage points. The finding matters because current video fake-news detectors rely on one modality or simple fusion and struggle with the mismatch between what is said, shown, and written in a video. If the claim holds, a single LLM can act as a general-purpose cross-modal reasoner for video misinformation without task-specific fusion architectures.

What carries the argument

The load-bearing object is the integrated prompt: outputs from a subtitle extractor (VSE), a speech recognizer (Whisper), and a visual-language model (CogVLM2) are concatenated with video metadata and fed to a LoRA-tuned LLM, which performs cross-modal attention over the combined text. The paper's argument is that converting all modalities into a shared textual space lets the LLM treat cross-modal consistency as a text-reasoning problem, so no specialized fusion network or modality-alignment training is needed. LoRA fine-tuning keeps the pretrained knowledge intact while adapting the model to the three-way classification of fake, real, and debunking content.

What would settle it

Retest VMID on videos released after the knowledge cutoff of the underlying LLM, or ablate the video-derived text (subtitles, audio, vision) and prompt with only title and metadata; if accuracy stays near 90 percent, the gain is recalled memorized fact-checks rather than multimodal detection.

Watch

Extended reading notes

Core claim

VMID establishes that a large language model, given a unified textual transcript of a video's subtitle stream, spoken audio, keyframe descriptions, and social metadata, can classify short videos as fake, real, or debunking more accurately than dedicated multimodal architectures. The paper reports 90.93% accuracy and 90.89% macro F1 on FakeSV, outperforming SV-FEND (81.05%) and other baselines by 9.87–19.6 absolute percentage points, and shows that the approach remains strong across different backbone LLMs (Qwen2.5, GLM4, InternLM2.5, Baichuan). A case study credits the LLM's external knowledge for catching a mislabeled police drill video, indicating the model leverages both video content and pretrained world knowledge.

Load-bearing premise

The results assume the LLM's pretrained knowledge does not already contain the ground-truth labels of the test videos; if the fact-check outcomes were in the training data, the reported accuracy would reflect memorization rather than detection.

Editorial extensions

If this is right

  • Fake-news detection for short video platforms can be implemented as a single LLM call plus three off-the-shelf extractors, replacing task-specific multimodal architectures.
  • Because the prompt is plain text, the same framework transfers to new video genres or languages by swapping the extractors, with no redesign of the fusion layer.
  • The large gains over SV-FEND and SVRPM suggest that cross-modal reasoning within an LLM captures inconsistencies that modality-tampering detectors miss.
  • Accuracy above 90% on FakeSV implies that the three-way distinction among fake, real, and debunking content is learnable from video-derived text alone when social context is included.

Reading between the lines

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

  • The reported margin may shrink on videos published after the LLM's training cutoff; a fair test would withhold post-cutoff examples or block parametric fact recall, since the case study shows the model using memorized knowledge.
  • The framework's dependence on the quality of the extractors means that Whisper transcription errors or CogVLM2 misdescriptions propagate directly into the prompt; an end-to-end noisy-input robustness study would clarify how much of the gain is genuine fusion versus extractor quality.
  • The same prompting strategy could be applied to longer-form video or to multimodal misinformation beyond news (health claims, product scams), reusing the identical pipeline with different metadata fields.
Share X Bluesky LinkedIn Reddit HN

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. The paper proposes VMID, a pipeline that extracts subtitle text (VSE), audio transcripts (Whisper), visual descriptions (CogVLM2), and metadata from short videos, concatenates these into a single natural-language prompt, and feeds the prompt to a LoRA-tuned LLM (Qwen2.5, GLM4, InternLM2.5, or Baichuan) for three-way classification as real, fake, or debunking. The central empirical claim is that on the FakeSV dataset VMID achieves 90.93% accuracy and 90.89% F1, outperforming SV-FEND (81.05%) and other baselines by roughly 9.87 to 19.6 absolute percentage points (Table I), with stable results across LLM backbones (Table II). The paper also presents two case studies, one correct and one incorrect prediction, and argues that VMID demonstrates the value of multimodal fusion and LLM knowledge for short-video misinformation detection.

Significance. Short-video misinformation detection is a timely and practically important problem, and the idea of normalizing heterogeneous modalities into a single text prompt for an LLM is a reasonable engineering contribution. If the reported gains over SV-FEND and SVRPM are real and attributable to the VMID fusion pipeline, the result would be practically useful. The paper's strengths are the breadth of baselines considered and the consistent performance across four LLM backbones in Table II. However, the empirical evaluation as written does not establish the claim: there is no contamination analysis, no error bars or significance tests, no train/test split details, no code release, and the one success case explicitly credits the LLM's parametric knowledge rather than the video content. The significance of the contribution therefore cannot be assessed until these gaps are closed.

major comments (4)
  1. [Section V-C, Table I; Section IV] The main result is reported as a single run without any description of the train/test split, the number of videos, the LoRA hyperparameters (rank, learning rate, number of epochs, batch size), or actual values for the parameters that appear as placeholders in Equations (8) and (10) (“segment duration seconds”, “filter threshold”). Section III-B explicitly says the method is described “without delving into specific implementation details,” but the paper's second contribution claims a full implementation. Without these details, with no error bars or significance tests, and with no code or data release, the 90.93% accuracy cannot be independently verified. Please provide the complete configuration, release the code, and report mean and standard deviation over multiple runs.
  2. [Section V-D, Fig. 5(b); Section III-B5] The success case in Fig. 5(b) credits “the knowledge base of the large language model” for the correct detection and contrasts VMID with SV-FEND, which lacks “external knowledge support.” Because FakeSV is built from real short-video posts with known fact-check outcomes, the web-scale pretraining corpora of Qwen2.5 or GLM4 may contain those very outcomes. The paper reports no contamination check, so the large improvement over non-LLM baselines could reflect parametric memorization of test labels rather than VMID's multimodal fusion. This is load-bearing: please add a leakage analysis, for example zero-shot evaluation of the base LLM with and without the multimodal prompt, ablations withholding each modality, and tests on videos from after the LLM's knowledge cutoff.
  3. [Section V-C, Table I] The caption of Table I says the results are “on our short video rumor dataset and the FakeSV dataset,” but the text only describes FakeSV; it is unclear whether the baselines were retrained on the same split or whether the numbers were taken from the original papers. Table I also reports no variance, and the text in Section V-C.2 refers to “The FakeSV model” when it apparently means SV-FEND. Please clarify the evaluation protocol, report per-class performance (real/fake/debunking) in addition to the aggregate metrics, and state explicitly which baselines were reproduced and under what settings.
  4. [Section V, overall; Section V-D] The central claim is that VMID “successfully integrates multimodal features,” but no ablation removes audio, visual, subtitle, or metadata components, and there is no comparison to a text-only LLM prompt on the same data. The reported accuracy could largely come from the title/subtitle text or from the LLM's prior, rather than from multimodal fusion. The two case studies, one success and one failure, are anecdotal and do not substitute for a quantitative ablation. Please add an ablation table and a text-only baseline to substantiate the multimodal-fusion attribution.
minor comments (7)
  1. [Section V-B] The section begins with an orphaned sentence, “Through this backtracking analysis, peak attention aligns with the item most pertinent to the query,” which appears to be leftover text from another source and should be removed.
  2. [Section III-B3] The SwiGLU formula is written as \(SwiGLU(x) = x \odot \sigma(W_1 x + b_1) + W_2 x + b_2\), which does not match the standard SwiGLU definition; please correct the equation.
  3. [Section III-B2, Eq. (3)] The Mel spectrogram formula is incomplete: the summation index \(k\) and the term \(h_m(t,f)\) are not defined with proper short-time Fourier transform frame indexing and Mel filterbank specifications.
  4. [Section III-B2, Eq. (5)] In Equation (5), both \(score(seq)\) and the set \(C\) of candidate decoding sequences are left undefined; please define them for clarity.
  5. [Section III-B6] The text says “we learn the weight coefficients \(\alpha\) and \(\beta\)” for modality importance, but these coefficients are never defined or used in Equations (6) or (7); please remove or operationalize them.
  6. [Figure 3 and its caption] The caption says videos are differentiated by yellow, gray, and blue backgrounds, but this color coding is difficult to read in the dense table and is inaccessible in grayscale printing; consider adding explicit labels for each row.
  7. [References] Reference [1] lists “Association for Computing Machinery” as the publisher for the AAAI FakeSV paper; the publisher and venue should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No formal circularity found; the reported FakeSV gains rest on a standard supervised evaluation, though LLM pretraining contamination remains an external validity risk rather than a circular derivation.

full rationale

VMID's claimed derivation is a standard extract-then-classify chain: VSE, Whisper, and CogVLM2 produce subtitle text, audio transcript, and visual descriptions; metadata is added; these are concatenated (Eq. 6) into a prompt; and a LoRA-tuned LLM (Eq. 7) emits the label. Nothing in this chain fits a parameter to the FakeSV test labels and then re-predicts them: the LoRA update is trained on the task data, and the extractors are fixed pretrained components. No load-bearing self-citations or author-imported uniqueness theorems appear; all cited tools and baselines are external. The one substantive concern, that Qwen2.5 or GLM4 pretraining may contain FakeSV fact-check outcomes, is a data-contamination and benchmark-validity risk, not a circularity: the paper's equations do not define detection in terms of the labels, and no exhibited reduction shows the test accuracy is forced by construction. If evidence of contamination were found, the empirical claim would be invalid, but the derivation would still not be circular in the formal sense. The case study's attribution of a correct prediction to the LLM's knowledge base is anecdotal and does not by itself make the evaluation circular.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities, but it relies on several unstated or unreported hyperparameters (lambda, segment duration, frame count, thresholds) and on the assumption that text transcriptions preserve misinformation cues and that the LLM has not memorized the test labels.

free parameters (5)
  • LoRA update scalar lambda
    Controls the intensity of the LoRA update in Eq. (7); the paper says it is tuned by cross-validation but reports no value.
  • segment duration seconds
    Used in Eq. (8) to divide videos into segments; the paper gives no value.
  • number of frames per segment Nf
    Used in Eq. (9) for uniform frame selection; no value reported.
  • cosine similarity filter threshold
    Used in Eq. (10) to discard similar frames; no value reported.
  • keyframe difference threshold
    Predefined threshold in the frame-difference method for keyframe selection (Section III-B.3); no value stated.
assumptions (4)
  • domain assumption The labels in the FakeSV dataset correctly represent real, fake, and debunking content.
    The evaluation takes the dataset labels as ground truth without external verification.
  • domain assumption Converting all modalities to text (OCR, ASR, image captions) preserves the information needed to detect misinformation.
    The method discards raw visual and audio signals and relies on text descriptions; this lossiness is not tested.
  • domain assumption The LLM's pretrained knowledge does not contain the ground-truth labels of the test videos.
    The case study credits the LLM's knowledge base for a correct prediction, raising the possibility of label leakage from pretraining.
  • domain assumption The off-the-shelf models (Whisper, CogVLM2, VSE) produce accurate enough transcripts and descriptions on video data.
    The pipeline's upper bound depends on the quality of these pretrained components; no error analysis is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos." pith.science (2026). https://pith.science/paper/U4LRF4Q3

@misc{pith2026241110032,
  author       = {Pith},
  title        = {Pith review of: VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U4LRF4Q3}},
  note         = {Machine review of arXiv:2411.10032}
}
read the original abstract

Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for the rapid spread of misinformation, as fake news and rumors can leverage the visual appeal and wide reach of short videos to circulate extensively among audiences. Existing fake news detection methods mainly rely on single-modal information, such as text or images, or apply only basic fusion techniques, limiting their ability to handle the complex, multi-layered information inherent in short videos. To address these limitations, this paper presents a novel fake news detection method based on multimodal information, designed to identify misinformation through a multi-level analysis of video content. This approach effectively utilizes different modal representations to generate a unified textual description, which is then fed into a large language model for comprehensive evaluation. The proposed framework successfully integrates multimodal features within videos, significantly enhancing the accuracy and reliability of fake news detection. Experimental results demonstrate that the proposed approach outperforms existing models in terms of accuracy, robustness, and utilization of multimodal information, achieving an accuracy of 90.93%, which is significantly higher than the best baseline model (SV-FEND) at 81.05%. Furthermore, case studies provide additional evidence of the effectiveness of the approach in accurately distinguishing between fake news, debunking content, and real incidents, highlighting its reliability and robustness in real-world applications.

Figures

Figures reproduced from arXiv: 2411.10032 by the authors.

Figure 1
Figure 1. Architecture of the proposed framework VMID.The proposed approach integrates multiple modalities to generate text [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of the Text Processing Workflow. The process [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The short video detection examples presented are differentiated by background colors to represent the nature of the video [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Loss curves for fine-tuning with LoRA on various [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Two representative fake news videos from FakeSV, [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A New Dataset and Benchmark for Grounding Multimodal Misinformation

    cs.SI 2025-09 conditional novelty 6.0 of 10

    GroundLie360 is a 2,044-video Snopes-derived benchmark with fine-grained annotations localizing six types of multimodal misinformation; the VLM prompting baseline FakeMark shows the task remains difficult.

Reference graph

Works this paper leans on

37 extracted references · 26 canonical work pages · cited by 1 Pith paper

  1. [1]

    Fakesv: A multimodal benchmark with rich social context for fake news detection on short video platforms,

    P. Qi, Y . Bu, J. Cao, W. Ji, R. Shui, J. Xiao, D. Wang, and T.-S. Chua, “Fakesv: A multimodal benchmark with rich social context for fake news detection on short video platforms,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 12. Association for Computing Machinery, 2023, pp. 14 444 – 14 452. [Online]. Available: https:/...

    work page 2023

  2. [2]

    News use across social media platforms 2018,

    A. Smith and M. Anderson, “News use across social media platforms 2018,” Pew Research Center , 2018. [Online]. Available: https: //www.journalism.org/2018/09/10/news-use-across- social-media-platforms-2018 /

    work page 2018

  3. [3]

    This analysis shows how viral fake election news stories outperformed real news on facebook,

    C. Silverman, “This analysis shows how viral fake election news stories outperformed real news on facebook,” BuzzFeed News , 2016. [Online]. Avail- able: https: //www.buzzfeednews.com/article/craigsilverman/viral- fake-election-news-outperformed-real-news-on-facebook

    work page 2016

  4. [4]

    Bogus ap tweet about explosion at white house wipes billions off us markets,

    J. Barron, “Bogus ap tweet about explosion at white house wipes billions off us markets,” ”The Telegraph”, 2013. [Online]. Available: https://www.telegraph.co.uk/finance/markets/10013768/Bogus-AP- tweet-about-explosion-at-the-White-House-wipes-billions-off-US- markets.html

    work page arXiv 2013

  5. [5]

    As mob lynchings fueled by whatsapp sweep india, authorities struggle to combat fake news,

    P. Chauhan, “As mob lynchings fueled by whatsapp sweep india, authorities struggle to combat fake news,” The Washington Post , 2018. [Online]. Available: https://www.washingtonpost.com/world/asiapacific/as-mob- lynchingsfueled-by-whatsapp-sweep-india-authorities-struggle- to-combat-fakenews/2018/07/02/683a1578-7bba-11e8-ac4e- 421ef7165923 story.html

    work page 2018

  6. [6]

    New insights into the social rumor characteristics during the covid- 19 pandemic in china,

    W. Lv, W. Zhou, B. Gao, Y . Han, and H. Fang, “New insights into the social rumor characteristics during the covid- 19 pandemic in china,” Frontiers in Public Health , vol. 10,

    work page

  7. [7]

    Understanding user profiles on social media for fake news detection,

    K. Shu, S. Wang, and H. Liu, “Understanding user profiles on social media for fake news detection,” in2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) , 2018, pp. 430–435

    work page 2018

  8. [8]

    Invid verification plugin,

    I. Project, “Invid verification plugin,” 2024, accessed: 2024- 11-09. [Online]. Available: https: //www.invid-project.eu/tools-and- services/invid-verification-plugin/

    work page 2024

Show all 37 references
  1. [9]

    Information credibility on twitter,

    C. Castillo, M. Mendoza, and B. Poblete, “Information credibility on twitter,” in Proceedings of the 20th International Conference on World Wide Web, ser. WWW ’11. New York, NY , USA: Association for Computing Machinery, 2011, p. 675–684. [Online]. Available: https://doi.org/1...

    2011

  2. [10]

    Detecting fake news by image analysis,

    E. Masciari, V . Moscato, A. Picariello, and G. Sperl ´ı, “Detecting fake news by image analysis,” in Proceedings of the 24th Symposium on International Database Engineering & Applications , ser. IDEAS ’20. New York, NY , USA: Association for Computing Machinery, 2020. [Online...

    2020

  3. [11]

    defend: Explainable fake news detection,

    K. Shu, L. Cui, S. Wang, D. Lee, and H. Liu, “defend: Explainable fake news detection,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’19. New York, NY , USA: Association for Computing Machinery, 2019, p. 395–405...

    2019

  4. [12]

    Sentiment aware fake news detection on online social networks,

    O. Ajao, D. Bhowmik, and S. Zargari, “Sentiment aware fake news detection on online social networks,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 2507–2511

    2019

  5. [13]

    Rumor detection with diverse counterfactual evidence,

    K. Zhang, J. Yu, H. Shi, J. Liang, and X.-Y . Zhang, “Rumor detection with diverse counterfactual evidence,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 3321–3331

    2023

  6. [14]

    Robust speech recognition via large-scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” 2022. [Online]. Available: https: //arxiv.org/abs/2212. 04356

    2022

  7. [15]

    Cogvlm2: Visual language models for image and video understanding,

    W. Hong, W. Wang, M. Ding, W. Yu, Q. Lv, Y . Wang, Y . Cheng, S. Huang, J. Ji, Z. Xue, L. Zhao, Z. Yang, X. Gu, X. Zhang, G. Feng, D. Yin, Z. Wang, J. Qi, X. Song, P. Zhang, D. Liu, B. Xu, J. Li, Y . Dong, and J. Tang, “Cogvlm2: Visual language models for image and video under...

    2024 arXiv

  8. [16]

    Video Subtitle Extractor,

    Y . FANG, “Video Subtitle Extractor,” https: //github. com/YaoFANGUK/video-subtitle-extractor, 2024, accessed: 2024-11- 09

    2024

  9. [17]

    Interpretable short video rumor detection based on modality tampering,

    K. Wu, Y . Lin, D. Cao, and D. Lin, “Interpretable short video rumor detection based on modality tampering,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024, pp. 9180–9189

    2024

  10. [18]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” 2021. [Online]. Available: https://arxi...

    2021 arXiv

  11. [19]

    Towards automatic detection of misinformation in online medical videos,

    R. Hou, V . Perez-Rosas, S. Loeb, and R. Mihalcea, “Towards automatic detection of misinformation in online medical videos,” in 2019 International Conference on Multimodal Interaction , ser. ICMI ’19. New York, NY , USA: Association for Computing Machinery, 2020, p. 235–243. [...

    2019 doi

  12. [20]

    NLP- based feature extraction for the detection of COVID-19 misinformation videos on YouTube,

    J. C. Medina Serrano, O. Papakyriakopoulos, and S. Hegelich, “NLP- based feature extraction for the detection of COVID-19 misinformation videos on YouTube,” in Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 , K. Verspoor, K. B. Cohen, M. Dredze, E. Ferrara, J....

    2020

  13. [21]

    Using topic modeling and adversarial neural networks for fake news video detection,

    H. Choi and Y . Ko, “Using topic modeling and adversarial neural networks for fake news video detection,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ser. CIKM ’21. New York, NY , USA: Association for Computing Machinery, 2021...

    2021

  14. [22]

    A multimodal misinfor- mation detector for covid-19 short videos on tiktok,

    L. Shang, Z. Kou, Y . Zhang, and D. Wang, “A multimodal misinfor- mation detector for covid-19 short videos on tiktok,” in 2021 IEEE international conference on big data (big data) . IEEE, 2021, pp. 899– 908

    2021

  15. [23]

    Baichuan2,

    B. Inc., “Baichuan2,” https: //github.com/baichuan-inc/Baichuan2, 2024, accessed: 2024-11-11

    2024

  16. [24]

    THUDM, “Glm-4,” https: //github.com/THUDM/GLM-4, 2024, ac- cessed: 2024-11-11

    2024

  17. [25]

    Internlm,

    InternLM, “Internlm,” https: //github.com/InternLM/InternLM, 2024, accessed: 2024-11-11

    2024

  18. [26]

    Qwen2.5,

    QwenLM, “Qwen2.5,” https: //github.com/QwenLM/Qwen2.5, 2024, accessed: 2024-11-11

    2024

  19. [27]

    A corpus of debunked and verified user-generated videos,

    O. Papadopoulou, M. Zampoglou, S. Papadopoulos, and Y . Kompatsiaris, “A corpus of debunked and verified user-generated videos,” Online Inf. Rev., vol. 43, pp. 72–88, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:69953011

    2019

  20. [28]

    A cnn-based misleading video detection model,

    X. Li, X. Xiao, J. Li, C. Hu, J. Yao, and S. Li, “A cnn-based misleading video detection model,” Scientific Reports, vol. 12, no. 1, p. 6092, 2022

    2022

  21. [29]

    Mgat-esm: Multi-channel graph attention neural network with event-sharing module for rumor detection,

    H. Ran, C. Jia, P. Zhang, and X. Li, “Mgat-esm: Multi-channel graph attention neural network with event-sharing module for rumor detection,” Information Sciences, vol. 592, pp. 402–416, 2022

    2022

  22. [30]

    Nlp-based feature extraction for the detection of covid-19 misinformation videos on youtube,

    J. C. M. Serrano, O. Papakyriakopoulos, and S. Hegelich, “Nlp-based feature extraction for the detection of covid-19 misinformation videos on youtube,” in Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 , 2020

    2020

  23. [31]

    Improving fake news detection by using an entity-enhanced framework to fuse diverse multimodal clues,

    P. Qi, J. Cao, X. Li, H. Liu, Q. Sheng, X. Mi, Q. He, Y . Lv, C. Guo, and Y . Yu, “Improving fake news detection by using an entity-enhanced framework to fuse diverse multimodal clues,” in Proceedings of the 29th ACM International Conference on Multimedia, ser. MM ’21. New Yor...

    2021

  24. [32]

    Wlan-log-based superspreader detection in the covid-19 pandemic,

    C. Zhang, Y . Pan, Y . Zhang, A. C. Champion, Z. Shen, D. Xuan, Z. Lin, and N. B. Shroff, “Wlan-log-based superspreader detection in the covid-19 pandemic,” High-Confidence Computing , vol. 1, no. 1, p. 100005, 2021. [Online]. Available: https: //www.sciencedirect. com/science...

    2021

  25. [33]

    User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user,

    R. Ranjan and S. S. Kumar, “User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user,” High-Confidence Computing , vol. 2, no. 1, p. 100034, 2022. [Online]. Available: https: //www.sciencedirect. com/science/article/pii...

    2022

  26. [34]

    Defending against neural fake news,

    R. Zellers, A. Holtzman, H. Rashkin, Y . Bisk, A. Farhadi, F. Roesner, and Y . Choi, “Defending against neural fake news,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch ´e-Buc, E. Fox, and R. Garnett, Eds., vol. 32....

    2019

  27. [35]

    Spotfake+: A multimodal framework for fake news detection via transfer learning (student abstract),

    S. Singhal, A. Kabra, M. Sharma, R. R. Shah, T. Chakraborty, and P. Kumaraguru, “Spotfake+: A multimodal framework for fake news detection via transfer learning (student abstract),” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 10, 2020, pp. 1...

    2020

  28. [36]

    Fakegpt: Fake news generation, explanation and detection of large language models,

    Y . Huang and L. Sun, “Fakegpt: Fake news generation, explanation and detection of large language models,” 2024. [Online]. Available: https://arxiv.org/abs/2310.05046 13

    2024 arXiv

  29. [2022]

    Available: https://www.frontiersin.org/journals/public- health/articles/10.3389/fpubh.2022.864955

    [Online]. Available: https://www.frontiersin.org/journals/public- health/articles/10.3389/fpubh.2022.864955

    2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.