Pith. sign in

REVIEW 5 major objections 6 minor 1 cited by

Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read This paper introduces fake news video explanation as a new task, with a 2,672-post dataset and a graph-based model that outperforms existing baselines.

desk verdict A useful first dataset and task for explaining fake news videos, but the gold labels inherit veracity from source sets without re-verification; fix that and the access issue before treating it as a benchmark. read the letter →

arxiv 2501.08514 v3 pith:NSOMM6JF submitted 2025-01-15 cs.CV cs.MM

Lizhi Chen , Zhong Qian , Peifeng Li , Qiaoming Zhu This is my paper · ORCID
classification cs.CVcs.MM
keywords fakenewsvideoexplanationVEdatasetnaturallanguagemultimodalmisinformationRelationGraphTransformerveracityexplainableAIdetection
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 proposes a new task, fake news video explanation (FNVE): given a news post with a title, video frames, and audio transcript, a system should generate a natural language explanation of why the post is false, rather than merely classifying it. To support this task the authors built FakeVE, a dataset of 2,672 fake news video posts with human-written explanations covering four aspects of falsity: contextual dishonesty, splice tampering, synthetic voiceover, and contrived absurdity. They also present a Multimodal Relation Graph Transformer (MRGT) that builds a graph over title, frame, and transcript nodes and uses graph propagation plus a BART decoder to generate explanations. Their experiments report that MRGT outperforms existing multimodal language models and fine-tuned baselines on standard generation metrics, and that manual annotations score higher than model explanations on LLM-based quality evaluation. If correct, this establishes the first benchmark for explainable fake news video analysis and a reusable resource for training verification systems.

What carries the argument

The central objects are the FakeVE dataset and the MRGT architecture. The dataset supplies 2,672 posts labeled with one of four falsity aspects and a human reference explanation; the four-aspect taxonomy is the annotation scheme that organizes what counts as an explanation. The MRGT carries the argument: it concatenates title, sampled video frames, and audio transcript tokens, encodes them with a Transformer, builds a multimodal relation graph whose edges link semantically related text tokens, frames matched to title tokens by cosine similarity, and frames to transcripts, then applies a graph convolutional network with a residual connection before a BART decoder generates the explanation autoregressively.

What would settle it

A fact-checking audit of a random sample of FakeVE posts against independent external sources would settle whether the gold explanations are reliable; a human preference study comparing MRGT explanations with GPT-4o's on accuracy and usefulness would settle whether the model's claimed advantage is real.

Watch

Extended reading notes

Core claim

The paper's central claim is that the falsity of a multimodal news video can be captured in a short natural language explanation, and that this explanation can be generated automatically from the title, video frames, and audio transcript. This claim rests on FakeVE, which the authors present as the first dataset to definitively explain four real-life fake news video aspects, with 2,672 posts inherited from existing fake news video datasets and explanations written by trained annotators. The paper further claims that MRGT, by representing cross-modal relations as a graph and propagating information through a graph convolutional network before decoding with BART, has a significant advantage in capturing the multimodal feature association of video and generating logically coherent explanatory text, supported by higher BLEU, ROUGE, METEOR and Sentence-BERT scores than all compared baselines and by ablations showing the graph and each modality contribute. It also claims that the human annotations are high quality, with inter-annotator agreement of 0.865 and higher G-Eval scores than GPT-4o and Qwen2-VL on persuasiveness, informativeness, soundness, and readability.

Load-bearing premise

The load-bearing premise is that every one of the 2,672 posts inherited from FMNV and FakeTT is genuinely fake, and that an annotator watching the video and reading the title can identify the falsity without external fact-checking.

Editorial extensions

If this is right

  • FNVE becomes an available benchmark task: future work can train and compare explanation generators on FakeVE using standard metrics.
  • The four-aspect taxonomy gives content moderation systems a concrete vocabulary for why a video is false, not just that it is false.
  • The dataset's explanations can be used as fine-grained evidence chains to train models to recognize specific tampering techniques such as splice tampering and synthetic voiceover.
  • The reported results imply that combining a relation graph with a pretrained encoder-decoder improves explanation coherence enough to beat strong multimodal language models on this task.

Reading between the lines

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

  • The four-aspect taxonomy could be extended to other languages and platforms, but the annotation protocol would need external fact-checking to verify the source labels.
  • The paper's evaluation relies on automatic n-gram metrics and G-Eval; a human preference study on explanation accuracy would test whether MRGT's higher scores correspond to more useful explanations.
  • A natural testable extension would be to use the explanations as supervision for detecting the specific manipulation type, not just generating text.
  • Because FakeVE inherits its veracity labels from FMNV and FakeTT, the dataset's quality is bounded by the accuracy of those source labels; a fact-check audit could quantify that bound.
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

5 major / 6 minor

Summary. The paper introduces the Fake News Video Explanation (FNVE) task, in which a multimodal news post (title, video frames, audio transcript) is used to generate a natural-language explanation of why the news video is false. The authors construct FakeVE, a dataset of 2,672 fake news video posts collected from the existing FMNV and FakeTT datasets, and manually annotate each post with an explanation and one of four error aspects (Contextual Dishonesty, Splice Tampering, Synthetic Voiceover, Contrived Absurdity). They report annotation statistics, analyze aspect distributions, and propose a Multimodal Relation Graph Transformer (MRGT) that combines a Transformer encoder, a graph convolutional network over a multimodal relation graph, and a BART decoder as a benchmark model. Experiments compare MRGT against several MLLM and fine-tuned baselines, with additional ablation studies on modality and graph contributions.

Significance. If the dataset labels are reliable, FakeVE would be a valuable first resource for the new task of natural-language explanation of fake news videos, going beyond binary veracity classification. The paper is clearly useful to the community in proposing a concrete task formulation, a detailed annotation protocol with good inter-annotator agreement (reported Cohen's kappa 0.865), and a reproducible baseline architecture. The exploratory analysis of aspect distribution and the qualitative case studies are helpful for understanding the task. However, the significance is conditional on two load-bearing assumptions: that the inherited veracity labels from FMNV and FakeTT are correct without independent re-verification, and that the LLM-based G-Eval scores used to validate annotation quality are themselves trustworthy. The absence of a dataset release mechanism also limits the community's ability to audit the resource.

major comments (5)
  1. [Section 3.1] The veracity labels of all 2,672 posts are inherited from FMNV and FakeTT without independent re-verification. The annotation instructions ask annotators to watch the video and check the title, but do not ask them to consult external sources or to independently determine whether the post is actually false. If any source-label false positives exist (e.g., true posts with exaggerated titles, opinion content, or satire), the gold explanations will be invalid, and both the annotation-quality evaluation in Section 3.3 and the supervised training signal for MRGT in Sections 5.3 and 5.4 will be contaminated. Please add a label re-verification step (e.g., fact-checking a random sample against external sources, or a two-stage annotation in which annotators first confirm falsity) and report the resulting agreement or disagreement rates.
  2. [Section 3.3.1 and Figure 4] The evaluation of annotation quality and of generated explanations relies entirely on G-Eval, an LLM-based referenceless scorer, with no human validation of G-Eval's ratings on this specific domain and no error bars or statistical tests. Figure 4 reports scores such as 4.61 for annotators, but without knowing whether G-Eval agrees with human judgments for this task, the claim that 'manual annotation performs well' is not supported. Similarly, the model comparisons in Section 5.5 use the same unvalidated G-Eval protocol. Please add a human evaluation on a sample of explanations, report inter-judge agreement, and include variance or significance tests for the claim that MRGT has a 'significant advantage.'
  3. [Section 4.2, Eq. (2)] The construction of the multimodal relation graph is not fully specified. The three rules in Section 4.2 state that 'semantically related text nodes' are linked via dependency tree relations, that edges are added between video frames and title tokens with 'highest modal similarity measured by cosine similarity,' and that 'visual nodes' are linked by adding an edge between video frame and audio transcript. It is unclear which tokens count as nodes, how many edges are added per rule, what threshold is used for cosine similarity, and how dependency relations apply to video-frame nodes. Without an exact adjacency-matrix definition, MRGT is not reproducible, and the source of the improvement attributed to the graph in Table 3 cannot be independently assessed. Please provide the full formal construction of A and include the release of code that implements it.
  4. [Section 1, Contribution bullet] The paper states 'we release our code and dataset,' but no repository URL, DOI, or other access mechanism is provided anywhere in the manuscript. Since the paper's central contribution is a new dataset, the community cannot audit the inherited labels, the annotation protocol, or the benchmark results without access to the data. Please include a working anonymous link or an availability statement in the camera-ready version.
  5. [Section 5.2 and Table 2] Table 2 reports single-run scores on a test set of only 267 posts, and the differences between MRGT and the best baseline are small on several metrics (e.g., BLEU-4 8.21 vs. 6.70 for GPT-4o, METEOR 91.62 vs. 86.68 for AMFM). No confidence intervals, multiple seeds, or significance tests are reported. The abstract's claim that MRGT 'has a significant advantage' therefore needs statistical support, especially given that the test set is small and the baselines include zero-shot MLLMs that were not fine-tuned.
minor comments (6)
  1. [Section 3.1] The paper reports 'Cohen's Kappa coefficient (Fleiss, 1971)' but the Fleiss reference is for Fleiss' kappa for multiple raters; please clarify whether Cohen's kappa or Fleiss' kappa was computed and over how many annotators.
  2. [Figure 2 and Section 3.2] There are inconsistencies in the abbreviations: Figure 2(a) uses 'CDS' while the text uses 'CD,' and 'the minimum number of cas' should be 'CA.'
  3. [Sections 5.1 and 5.3] The baseline names are inconsistent: Section 5.3 refers to 'PTSN' while Section 5.1 and Table 2 list 'NCT,' and the model is written as both 'HAA V' and 'HAAV.'
  4. [Section 4.3] The term 'spurious explanation' appears several times (e.g., 'generate spurious explanations,' 'spurious inference') and should be replaced with 'generated explanation' or 'target explanation.' These appear to be translation artifacts.
  5. [Section 3.3.2] The prompt template P is described with placeholders but the actual prompt sent to the MLLMs is not shown in full; please provide the complete template and the formatting used for the audio transcript and video frames.
  6. [Throughout] There are several remaining typos and awkward phrasings, including 'Pering a news video' (Section 3.1), 'utuber' (Section 3.1), and 'violates basic common sense or logical errors' (Section 3.2). A careful proofreading pass is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dataset construction and MRGT benchmark are self-contained; inherited veracity labels are a data-quality concern, not a circular reduction.

full rationale

FakeVE is assembled from two existing datasets (FMNV and FakeTT), and the gold explanations are produced by human annotators following instructions; no parameter is fitted to the target explanations and then reported as a prediction. MRGT is trained with cross-entropy on a training split and evaluated on a held-out test split (Section 5.2), and the multimodal graph edges in Eq. (1)-(3) are computed from input modalities (title, frames, transcript), not from the gold explanation Y. The claimed MRGT advantage is supported by BLEU/ROUGE/METEOR/SentBERT comparisons and ablation in Tables 2-3, so the benchmark does not reduce to its inputs. The one same-group citation (FMNV, Wang et al. 2025) supplies 1,500 inherited fake labels and the annotation scheme does not re-verify them against external sources; this is a label-validity limitation of the dataset, but it is not a circular derivation in the sense of Eq. X = Eq. Y or a fitted quantity renamed as a prediction. No uniqueness theorem or ansatz is imported from the authors' prior work.

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

The central dataset claim rests on inherited source labels, annotator judgment without external fact-checking, an author-defined taxonomy, and LLM-based evaluation. These are domain assumptions rather than derived results.

assumptions (4)
  • domain assumption The veracity labels in source datasets FMNV and FakeTT are correct.
    FakeVE is constructed by taking 2,672 fake posts from these datasets (Section 3.1); if source labels are wrong, the explanations inherit the error.
  • domain assumption Annotators can determine why a video is false from the title, video frames, and audio transcript alone.
    The annotation scheme in Section 3.1 only instructs annotators to watch the video and check the title; it does not mention external fact-checking or source verification.
  • ad hoc to paper The four aspects (Contextual Dishonesty, Splice Tampering, Synthetic Voiceover, Contrived Absurdity) are a complete and meaningful taxonomy for fake news video explanations.
    These categories are introduced by the authors in Section 3.1 without derivation from prior taxonomies; all annotations are forced into these four buckets.
  • domain assumption G-Eval scores from an LLM reflect the true quality of explanations.
    Section 3.3.1 uses G-Eval for referenceless evaluation on Persuasiveness, Informativeness, Soundness, and Readability, but no human validation or correlation study is reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation." pith.science (2026). https://pith.science/paper/NSOMM6JF

@misc{pith2026250108514,
  author       = {Pith},
  title        = {Pith review of: Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NSOMM6JF}},
  note         = {Machine review of arXiv:2501.08514}
}
read the original abstract

Multimodal fake news videos are difficult to interpret because they require comprehensive consideration of the correlation and consistency between multiple modes. Existing methods deal with fake news videos as a classification problem, but it's not clear why news videos are identified as fake. Without proper explanation, the end user may not understand the underlying meaning of the falsehood. Therefore, we propose a new problem - Fake news video Explanation (FNVE) - given a multimodal news post containing a video and title, our goal is to generate natural language explanations to reveal the falsity of the news video. To that end, we developed FakeVE, a new dataset of 2,672 fake news video posts that can definitively explain four real-life fake news video aspects. In order to understand the characteristics of fake news video explanation, we conducted an exploratory analysis of FakeVE from different perspectives. In addition, we propose a Multimodal Relation Graph Transformer (MRGT) based on the architecture of multimodal Transformer to benchmark FakeVE. The empirical results show that the results of the various benchmarks (adopted by FakeVE) are convincing and provide a detailed analysis of the differences in explanation generation of the benchmark models.

Figures

Figures reproduced from arXiv: 2501.08514 by the authors.

Figure 1
Figure 1. Example scenarios of multimodal fake news [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Statistics of four error aspects of FakeVE [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Evaluation results of explanation quality using [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Examples of the explainable aspects of four different errors analyzed by GPT-4 and the human annotators, [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: The architecture of the proposed MRGT, which consists of three key components: Multimodal Feature [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Evaluation results of explanation quality using [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Examples of good and bad explanations produced by MRGT and AMFM in the FakeVE dataset. [PITH_FULL_IMAGE:figures/full_fig_p010_8.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

42 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    Combating online misinformation videos: Characterization, detection, and future directions

    Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Combating online misinformation videos: Characterization, detection, and future directions. In Proceedings of the 31st ACM International Conference on Multimedia , pages 8770--8780, 2023

    work page 2023

  2. [2]

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

    Hyewon Choi and Youngjoong 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 , pages 2950--2954, 2021

    work page 2021

  3. [3]

    Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models

    Christos Christodoulou, Nikos Salamanos, Pantelitsa Leonidou, Michail Papadakis, and Michael Sirivianos. Identifying misinformation on youtube through transcript contextual analysis with transformer models. arXiv preprint arXiv:2307.12155 , 2023

    work page Pith review arXiv 2023

  4. [4]

    Meteor universal: Language specific translation evaluation for any target language

    Michael Denkowski and Alon Lavie. Meteor universal: Language specific translation evaluation for any target language. In Proceedings of the ninth workshop on statistical machine translation , pages 376--380, 2014

    work page 2014

  5. [5]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018

    arXiv 2018

  6. [6]

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

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 , 2020

    arXiv 2010

  7. [7]

    Market quavers after fake ap tweet says obama was hurt in white house explosions

    Dina ElBoghdady. Market quavers after fake ap tweet says obama was hurt in white house explosions. The Washington Post , 23, 2013

    work page 2013

  8. [8]

    Measuring nominal scale agreement among many raters

    Joseph L Fleiss. Measuring nominal scale agreement among many raters. Psychological bulletin , 76(5):378, 1971

    work page 1971

Show all 42 references
  1. [9]

    A novel method for detecting misinformation in videos, utilizing reverse image search, semantic analysis, and sentiment comparison of metadata

    Dhanvi Ganti. A novel method for detecting misinformation in videos, utilizing reverse image search, semantic analysis, and sentiment comparison of metadata. Utilizing Reverse Image Search, Semantic Analysis, and Sentiment Comparison of Metadata (June 5, 2022) , 2022

    2022

  2. [10]

    Generating visual explanations

    Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. Generating visual explanations. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part IV 14 , pages 3--19...

    2016

  3. [11]

    Towards automatic detection of misinformation in online medical videos

    Rui Hou, Ver \'o nica P \'e rez-Rosas, Stacy Loeb, and Rada Mihalcea. Towards automatic detection of misinformation in online medical videos. In 2019 International conference on multimodal interaction , pages 235--243, 2019

    2019

  4. [12]

    Misinformation detection on youtube using video captions

    Raj Jagtap, Abhinav Kumar, Rahul Goel, Shakshi Sharma, Rajesh Sharma, and Clint P George. Misinformation detection on youtube using video captions. arXiv preprint arXiv:2107.00941 , 2021

    2021 arXiv

  5. [13]

    e-vil: A dataset and benchmark for natural language explanations in vision-language tasks

    Maxime Kayser, Oana-Maria Camburu, Leonard Salewski, Cornelius Emde, Virginie Do, Zeynep Akata, and Thomas Lukasiewicz. e-vil: A dataset and benchmark for natural language explanations in vision-language tasks. In Proceedings of the IEEE/CVF international conference on compute...

    2021

  6. [14]

    Textual explanations for self-driving vehicles

    Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata. Textual explanations for self-driving vehicles. In Proceedings of the European conference on computer vision (ECCV) , pages 563--578, 2018

    2018

  7. [15]

    Richard Landis and Gary G

    J. Richard Landis and Gary G. Koch. The measurement of observer agreement for categorical data. Biometrics , page 159, Mar 1977

    1977

  8. [16]

    Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 , 2019

    1910 arXiv

  9. [17]

    Rouge: A package for automatic evaluation of summaries

    Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out , pages 74--81, 2004

    2004

  10. [18]

    Covid-vts: Fact extraction and verification on short video platforms

    Fuxiao Liu, Yaser Yacoob, and Abhinav Shrivastava. Covid-vts: Fact extraction and verification on short video platforms. arXiv preprint arXiv:2302.07919 , 2023

    2023 arXiv

  11. [19]

    Fixing weight decay regularization in adam

    Ilya Loshchilov and Frank Hutter. Fixing weight decay regularization in adam. 2018

    2018

  12. [20]

    Explainable ai approach towards toxic comment classification

    Aditya Mahajan, Divyank Shah, and Gibraan Jafar. Explainable ai approach towards toxic comment classification. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 2 , pages 849--858. Springer, 2021

    2020

  13. [21]

    Deep multi-task model for sarcasm detection and sentiment analysis in arabic language

    Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, and Ahmed Khoumsi. Deep multi-task model for sarcasm detection and sentiment analysis in arabic language. arXiv preprint arXiv:2106.12488 , 2021

    2021 arXiv

  14. [22]

    Multi-modal semantic inconsistency detection in social media news posts

    Scott McCrae, Kehan Wang, and Avideh Zakhor. Multi-modal semantic inconsistency detection in social media news posts. In International Conference on Multimedia Modeling , pages 331--343. Springer, 2022

    2022

  15. [23]

    The covid-19 ‘infodemic’: a new front for information professionals

    Salman Bin Naeem and Rubina Bhatti. The covid-19 ‘infodemic’: a new front for information professionals. Health Information & Libraries Journal , 37(3):233--239, 2020

    2020

  16. [24]

    Bleu: a method for automatic evaluation of machine translation

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics , pages 311--318, 2002

    2002

  17. [25]

    Toward safer highways, application of xgboost and shap for real-time accident detection and feature analysis

    Amir Bahador Parsa, Ali Movahedi, Homa Taghipour, Sybil Derrible, and Abolfazl Kouros Mohammadian. Toward safer highways, application of xgboost and shap for real-time accident detection and feature analysis. Accident Analysis & Prevention , 136:105405, 2020

    2020

  18. [26]

    Detecting harmful memes and their targets

    Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. Detecting harmful memes and their targets. arXiv preprint arXiv:2110.00413 , 2021

    2021 arXiv

  19. [27]

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

    Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng 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 , volume 3...

    2023

  20. [28]

    Hierarchical multi-modal contextual attention network for fake news detection

    Shengsheng Qian, Jinguang Wang, Jun Hu, Quan Fang, and Changsheng Xu. Hierarchical multi-modal contextual attention network for fake news detection. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval , pages 153--...

    2021

  21. [29]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , p...

    2021

  22. [30]

    Faster r-cnn: Towards real-time object detection with region proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems , 28, 2015

    2015

  23. [31]

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

    Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon 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

  24. [32]

    A multimodal misinformation detector for covid-19 short videos on tiktok

    Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A multimodal misinformation detector for covid-19 short videos on tiktok. In 2021 IEEE international conference on big data (big data) , pages 899--908. IEEE, 2021

    2021

  25. [33]

    Seeing is believing: Is video modality more powerful in spreading fake news via online messaging apps? Journal of Computer-Mediated Communication , 26(6):301--319, 2021

    S Shyam Sundar, Maria D Molina, and Eugene Cho. Seeing is believing: Is video modality more powerful in spreading fake news via online messaging apps? Journal of Computer-Mediated Communication , 26(6):301--319, 2021

    2021

  26. [34]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017

    2017

  27. [35]

    Cider: Consensus-based image description evaluation

    Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4566--4575, 2015

    2015

  28. [36]

    The challenges of studying misinformation on video-sharing platforms during crises and mass-convergence events

    Sukrit Venkatagiri, Joseph S Schafer, and Stephen Prochaska. The challenges of studying misinformation on video-sharing platforms during crises and mass-convergence events. arXiv preprint arXiv:2303.14309 , 2023

    2023 arXiv

  29. [37]

    Zhao, John Canny, and Avideh Zakhor

    Kehan Wang, David Chan, Seth Z. Zhao, John Canny, and Avideh Zakhor. Misinformation detection in social media video posts, 2022

    2022

  30. [38]

    Eann: Event adversarial neural networks for multi-modal fake news detection

    Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining , page...

    2018

  31. [39]

    The (minimal) persuasive advantage of political video over text

    Chloe Wittenberg, Ben M Tappin, Adam J Berinsky, and David G Rand. The (minimal) persuasive advantage of political video over text. Proceedings of the National Academy of Sciences , 118(47):e2114388118, 2021

    2021

  32. [40]

    Km-bart: Knowledge enhanced multimodal bart for visual commonsense generation

    Yiran Xing, Zai Shi, Zhao Meng, Gerhard Lakemeyer, Yunpu Ma, and Roger Wattenhofer. Km-bart: Knowledge enhanced multimodal bart for visual commonsense generation. arXiv preprint arXiv:2101.00419 , 2021

    2021 arXiv

  33. [41]

    Multimodal transformer for multimodal machine translation

    Shaowei Yao and Xiaojun Wan. Multimodal transformer for multimodal machine translation. In Proceedings of the 58th annual meeting of the association for computational linguistics , pages 4346--4350, 2020

    2020

  34. [42]

    Multi-modal fake news detection on social media via multi-grained information fusion

    Yangming Zhou, Yuzhou Yang, Qichao Ying, Zhenxing Qian, and Xinpeng Zhang. Multi-modal fake news detection on social media via multi-grained information fusion. In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval , pages 343--352, 2023

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.