research-article
Authors: Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, Jintao Li
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
Pages 1351 - 1360
Published: 28 October 2024 Publication History
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Abstract
As short-form video-sharing platforms become a significant channel for news consumption, fake news in short videos has emerged as a serious threat in the online information ecosystem, making developing detection methods for this new scenario an urgent need. Compared with that in text and image formats, fake news on short video platforms contains rich but heterogeneous information in various modalities, posing a challenge to effective feature utilization. Unlike existing works mostly focusing on analyzing what is presented, we introduce a novel perspective that considers how it might be created. Through the lens of the creative process behind news video production, our empirical analysis uncovers the unique characteristics of fake news videos in material selection and editing. Based on the obtained insights, we design FakingRecipe, a creative process-aware model for detecting fake news short videos. It captures the fake news preferences in material selection from sentimental and semantic aspects and considers the traits of material editing from spatial and temporal aspects. To improve evaluation comprehensiveness, we first construct FakeTT, an English dataset for this task, and conduct experiments on both FakeTT and the existing Chinese FakeSV dataset. The results show FakingRecipe's superiority in detecting fake news on short video platforms.
References
[1]
Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, and Hwalsuk Lee. 2019. Character Region Awareness for Text Detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9365--9374.
[2]
John A Bateman and Chiao-I Tseng. 2023. Multimodal discourse analysis as a method for revealing narrative strategies in news videos. Multimodal Communication, Vol. 12, 3 (2023), 261--285.
[3]
Jerome S Bruner. 2009. Actual minds, possible worlds. Harvard university press.
[4]
Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. 2023. Combating Online Misinformation Videos: Characterization, Detection, and Future Directions. In Proceedings of the 31st ACM International Conference on Multimedia. 8770--8780.
Digital Library
[5]
Yixuan Chen, Dongsheng Li, Peng Zhang, Jie Sui, Qin Lv, Lu Tun, and Li Shang. 2022. Cross-modal Ambiguity Learning for Multimodal Fake News Detection. In Proceedings of the ACM Web Conference 2022. 2897--2905.
Digital Library
[6]
Ziwei Chen, Linmei Hu, Weixin Li, Yingxia Shao, and Liqiang Nie. 2023. Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 627--638.
[7]
Hyewon Choi and Youngjoong Ko. 2021. Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2950--2954.
Digital Library
[8]
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116 (2019).
[9]
Angela Dobele, Adam Lindgreen, Michael Beverland, Joëlle Vanhamme, and Robert Van Wijk. 2007. Why pass on viral messages? Because they connect emotionally. Business Horizons, Vol. 50, 4 (2007), 291--304.
[10]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[11]
Monika Fludernik. 2009. An Introduction to Narratology. Routledge.
[12]
Dhanvi Ganti. 2022. A Novel Method for Detecting Misinformation in Videos, Utilizing Reverse Image Search, Semantic Analysis, and Sentiment Comparison of Metadata. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4128499.
[13]
Jeffrey Gottfried. 2024. Americans? Social Media Use. https://www.pewresearch.org/internet/2024/01/31/americans-social-media-use/.
[14]
Anne Hamby, Hongmin Kim, and Francesca Spezzano. 2024. Sensational stories: The role of narrative characteristics in distinguishing real and fake news and predicting their spread. Journal of Business Research, Vol. 170 (2024), 114289.
[15]
Jonathan Hendrickx. 2023. From Newspapers to TikTok: Social Media Journalism as the Fourth Wave of News Production, Diffusion and Consumption. In Blurring Boundaries of Journalism in Digital Media: New Actors, Models and Practices. Springer, 229--246.
[16]
Rui Hou, Verónica Pérez-Rosas, Stacy Loeb, and Rada Mihalcea. 2019. Towards Automatic Detection of Misinformation in Online Medical Videos. In 2019 International Conference on Multimodal Interaction. 235--243.
[17]
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. 2021. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 29 (2021), 3451--3460.
Digital Library
[18]
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.
[19]
Raj Jagtap, Abhinav Kumar, Rahul Goel, Shakshi Sharma, Rajesh Sharma, and Clint P George. 2021. Misinformation Detection on YouTube Using Video Captions. arXiv preprint arXiv:2107.00941 (2021).
[20]
Hamid Karimi and Jiliang Tang. 2019. Learning Hierarchical Discourse-level Structure for Fake News Detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 3432--3442.
[21]
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.
[22]
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. 2023. Segment Anything. arXiv:2304.02643 (2023).
[23]
Xiaojun Li, Xvhao Xiao, Jia Li, Changhua Hu, Junping Yao, and Shaochen Li. 2022. A CNN-based Misleading Video Detection Model. Scientific Reports, Vol. 12, 1 (2022), 1--9.
[24]
Bernhard Liebl and Manuel Burghardt. 2023. Designing a Prototype for Visual Exploration of Narrative Patterns in NewsVideos. (2023).
[25]
Fuxiao Liu, Yaser Yacoob, and Abhinav Shrivastava. 2023. COVID-VTS: Fact Extraction and Verification on Short Video Platforms. arXiv preprint arXiv:2302.07919 (2023).
[26]
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in neural information processing systems, Vol. 32 (2019).
[27]
Katerina Eva Matsa. 2023. More Americans are getting news on TikTok, bucking the trend seen on most other social media sites. https://www.pewresearch.org/short-reads/2023/11/15/more-americans-are-getting-news-on-tiktok-bucking-the-trend-seen-on-most-other-social-media-sites/.
[28]
Shuo Niu, Zhicong Lu, Amy X Zhang, Jie Cai, Carla F Griggio, and Hendrik Heuer. 2023. Building Credibility, Trust, and Safety on Video-Sharing Platforms. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1--7.
Digital Library
[29]
Shuo Niu, Dilasha Shrestha, Abhisan Ghimire, and Zhicong Lu. 2023. A Survey on Watching Social Issue Videos among YouTube and TikTok Users. arXiv preprint arXiv:2310.19193 (2023).
[30]
OpenAI. 2023. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774 (2023).
[31]
Priyank Palod, Ayush Patwari, Sudhanshu Bahety, Saurabh Bagchi, and Pawan Goyal. 2019. Misleading Metadata Detection on YouTube. In Advances in Information Retrieval: ECIR 2019. 140--147.
[32]
Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2017. Web Video Verification using Contextual Cues. In Proceedings of the 2nd International Workshop on Multimedia Forensics and Security. 6--10.
Digital Library
[33]
Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng Chua. 2023. 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. 14444--14452.
Digital Library
[34]
Peng Qi, Juan Cao, Xirong Li, Huan Liu, Qiang Sheng, Xiaoyue Mi, Qin He, Yongbiao Lv, Chenyang Guo, and Yingchao Yu. 2021. 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. 1212--1220.
Digital Library
[35]
Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, and Tat-Seng Chua. 2023. Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors. In Findings of the Association for Computational Linguistics: ACL 2023. 11947--11959.
[36]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning. PMLR, 8748--8763.
[37]
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-Chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, François Grondin, William Aris, Hwidong Na, Yan Gao, Renato De Mori, and Yoshua Bengio. 2021. SpeechBrain: A General-Purpose Speech Toolkit. arxiv: 2106.04624 [eess.AS] arXiv:2106.04624.
[38]
Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon Hegelich. 2020. 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.
[39]
Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. 2021. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In 2021 IEEE International Conference on Big Data. 899--908.
[40]
Qiang Sheng, Xueyao Zhang, Juan Cao, and Lei Zhong. 2021. Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1640--1650. https://doi.org/10.1145/3459637.3482440
Digital Library
[41]
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, Vol. 19, 1 (2017), 22--36.
Digital Library
[42]
S Shyam Sundar, Maria D Molina, and Eugene Cho. 2021. Seeing is believing: Is video modality more powerful in spreading fake news via online messaging apps? Journal of Computer-Mediated Communication, Vol. 26, 6 (2021), 301--319.
[43]
Chiao-I Tseng, Bernhard Liebl, Manuel Burghardt, and John Bateman. 2023. FakeNarratives - First Forays in Understanding Narratives of Disinformation in Public and Alternative News Videos. In Digital Humanities im deutschsprachigen Raum. 138.
[44]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. Advances in Neural Information Processing Systems, Vol. 30 (2017).
[45]
Karin Wahl-Jorgensen and Thomas R Schmidt. 2019. News and Storytelling. In The Handbook of Journalism Studies. Routledge, 261--276.
[46]
Guan Wang, Rebecca Frederick, Jinglong Duan, William Wong, Verica Rupar, Weihua Li, and Quan Bai. 2024. Detecting misinformation through Framing Theory: the Frame Element-based Model. arXiv preprint arXiv:2402.15525 (2024).
[47]
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2021. Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1288--1297.
Digital Library
[48]
Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. 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. 849--857.
Digital Library
[49]
Yang Wu, Pengwei Zhan, Yunjian Zhang, Liming Wang, and Zhen Xu. 2021. Multimodal Fusion with Co-Attention Networks for Fake News Detection. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2560--2569.
[50]
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. 2023. The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision). arXiv preprint arXiv:2309.17421 (2023).
[51]
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.
Digital Library
Index Terms
FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process
Information systems
Information systems applications
Multimedia information systems
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Published In
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai
Monash University, Australia
, - Mohan Kankanhalli
NUS, Singapore
, - Balakrishnan Prabhakaran
UT Dallas, USA
, - Susanne Boll
University of Oldenburg, Germany
, - Program Chairs:
- Ramanathan Subramanian
University of Canberra & IIT Ropar, Australia
, - Liang Zheng
Australian National University, Australia
, - Vivek K. Singh
Rutgers University, USA
, - Pablo Cesar
Centrum Wiskunde & Informatica, Netherlands
, - Lexing Xie
Australian National University, Australia
, - Dong Xu
University of Hong Kong, Hong Kong
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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- SIGMM: ACM Special Interest Group on Multimedia
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 October 2024
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Author Tags
- misinformation video detection
- multi-modal computing
Qualifiers
- Research-article
Funding Sources
- Postdoctoral Fellowship Program of CPSF
- International Postdoctoral Exchange Fellowship Program by Office of China Postdoc Council
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
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MM '24
Sponsor:
- SIGMM
MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
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Overall Acceptance Rate 995 of 4,171 submissions, 24%
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