research-article
Authors: Dong Zhou, Qiang Ouyang, Nankai Lin, Yongmei Zhou, Aimin Yang
ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 24, Issue 2
Article No.: 11, Pages 1 - 22
Published: 10 February 2025 Publication History
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Abstract
The prevalence of fake news online has become a significant societal concern. To combat this, multimodal detection techniques based on images and text have shown promise. Yet, these methods struggle to analyze complex relationships within and between modalities due to the diverse discriminative elements in the news content. In addition, research on multimodal and multi-class fake news detection remains insufficient. To address the above challenges, in this article, we propose a novel detection model, GS2F, leveraging graph structure and guided semantic fusion. Specifically, we construct a multimodal graph structure to align two modalities and employ graph contrastive learning for refined fusion representations. Furthermore, a guided semantic fusion module is introduced to maximize the utilization of single-modal information and a dynamic contribution assignment layer is designed to weigh the importance of image, text, and multimodal features. Experimental results on Fakeddit demonstrate that our model outperforms existing methods, marking a step forward in the multimodal and multi-class fake news detection.
References
[1]
Oluwaseun Ajao, Deepayan Bhowmik, and Shahrzad Zargari. 2019. Sentiment aware fake news detection on online social networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2507–2511.
[2]
Herley Shaori Al-Ash and Wahyu Catur Wibowo. 2018. Fake news identification characteristics using named entity recognition and phrase detection. In Proceedings of the 2018 10th International Conference on Information Technology and Electrical Engineering. IEEE, 12–17.
[3]
Alexandre Bovet and Hernán A Makse. 2019. Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications 10, 1 (2019), 7.
[4]
Michele Cantarella, Nicolò Fraccaroli, and Roberto Volpe. 2023. Does fake news affect voting behaviour?Research Policy 52, 1 (2023), 104628.
[5]
Tong Chen, Xue Li, Hongzhi Yin, and Jun Zhang. 2018. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers 22. Springer, 40–52.
[6]
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
[7]
Tsun-hin Cheung and Kin-man Lam. 2022. Crossmodal bipolar attention for multimodal classification on social media. Neurocomputing 514, 1 (2022), 1–12.
[8]
Deepjyoti Choudhury and Tapodhir Acharjee. 2023. A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers. Multimedia Tools and Applications 82, 6 (2023), 9029–9045.
Digital Library
[9]
Limeng Cui, Suhang Wang, and Dongwon Lee. 2019. Same: Sentiment-aware multi-modal embedding for detecting fake news. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 41–48.
Digital Library
[10]
Mudit Dhawan, Shakshi Sharma, Aditya Kadam, Rajesh Sharma, and Ponnurangam Kumaraguru. 2024. Game-on: Graph attention network based multimodal fusion for fake news detection. Social Network Analysis and Mining 14, 1 (2024), 114.
[11]
Xishuang Dong, Uboho Victor, and Lijun Qian. 2020. Two-path deep semisupervised learning for timely fake news detection. IEEE Transactions on Computational Social Systems 7, 6 (2020), 1386–1398.
[12]
Paolo Ferragina and Ugo Scaiella. 2010. Tagme: On-the-fly annotation of short text fragments (by wikipedia entities). In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 1625–1628.
Digital Library
[13]
Boyang Fu and Jie Sui. 2022. Multi-modal affine fusion network for social media rumor detection. PeerJ Computer Science 8, 1 (2022), e928.
[14]
Saqib Hakak, Mamoun Alazab, Suleman Khan, Thippa Reddy Gadekallu, and Wazir Zada Khan. 2020. An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems 117, 1 (2020), 47–58.
[15]
Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, and Yongbin Li. 2022. UniMSE: Towards unified multimodal sentiment analysis and emotion recognition. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 7837–7851.
[16]
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the NAACL-HLT. 4171–4186.
[17]
Syed Ali Khayam. 2003. The discrete cosine transform (DCT): Theory and application. Michigan State University 114, 1 (2003), 31.
[18]
Bo Li and Olan Scott. 2020. Fake news travels fast: Exploring misinformation circulated around wu lei’s coronavirus case. International Journal of Sport Communication 13, 3 (2020), 505–513.
[19]
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Proceedings of the International Conference on Machine Learning. PMLR, 12888–12900.
[20]
Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated graph sequence neural networks. In Proceedings of the ICLR’16.
[21]
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10012–10022.
[22]
Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 3818–3824.
[23]
Eric Müller-Budack, Jonas Theiner, Sebastian Diering, Maximilian Idahl, and Ralph Ewerth. 2020. Multimodal analytics for real-world news using measures of cross-modal entity consistency. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 16–25.
Digital Library
[24]
Kai Nakamura, Sharon Levy, and William Yang Wang. 2020. Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. In Proceedings of the 12th Language Resources and Evaluation Conference. 6149–6157.
[25]
Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2018. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 231–240.
[26]
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
[27]
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
[28]
Shengsheng Qian, Jinguang Wang, Jun Hu, Quan Fang, and Changsheng Xu. 2021. 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. 153–162.
Digital Library
[29]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, etal. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748–8763.
[30]
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2931–2937.
[31]
Julio CS Reis, André Correia, Fabrício Murai, Adriano Veloso, and Fabrício Benevenuto. 2019. Supervised learning for fake news detection. IEEE Intelligent Systems 34, 2 (2019), 76–81.
Digital Library
[32]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis Machine Intelligence 39, 6 (2017), 1137–1149.
[33]
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. 2019. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 658–666.
[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.
Digital Library
[35]
Vivek K Singh, Isha Ghosh, and Darshan Sonagara. 2021. Detecting fake news stories via multimodal analysis. Journal of the Association for Information Science and Technology 72, 1 (2021), 3–17.
Digital Library
[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 Intelligence. 13915–13916.
[37]
Shivangi Singhal, Tanisha Pandey, Saksham Mrig, Rajiv Ratn Shah, and Ponnurangam Kumaraguru. 2022. Leveraging intra and inter modality relationship for multimodal fake news detection. In Companion Proceedings of the Web Conference 2022. 726–734.
Digital Library
[38]
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818–2826.
[39]
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 30, 1 (2017), 6000–6010.
[40]
Bin Wang, Yong Feng, Xian-cai Xiong, Yong-heng Wang, and Bao-hua Qiang. 2023. Multi-modal transformer using two-level visual features for fake news detection. Applied Intelligence 53, 9 (2023), 10429–10443.
Digital Library
[41]
Haizhou Wang, Sen Wang, and YuHu Han. 2022. Detecting fake news on Chinese social media based on hybrid feature fusion method. Expert Systems with Applications 208, 1 (2022), 118111.
Digital Library
[42]
Jinxia Wang, Stanislav Makowski, Alan Cieślik, Haibin Lv, and Zhihan Lv. 2024. Fake news in virtual community, virtual society, and metaverse: A survey. IEEE Transactions on Computational Social Systems 11, 4 (2024), 4828–4842.
[43]
Longzheng Wang, Chuang Zhang, Hongbo Xu, Yongxiu Xu, Xiaohan Xu, and Siqi Wang. 2023. Cross-modal contrastive learning for multimodal fake news detection. In Proceedings of the 31st ACM International Conference on Multimedia. 5696–5704.
Digital Library
[44]
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 and Data Mining. 849–857.
Digital Library
[45]
Junfei Wu, Weizhi Xu, Qiang Liu, Shu Wu, and Liang Wang. 2023. Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks. IEEE Transactions on Knowledge and Data Engineering (2023).
[46]
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.
[47]
Shufeng Xiong, Guipei Zhang, Vishwash Batra, Lei Xi, Lei Shi, and Liangliang Liu. 2023. TRIMOON: Two-round inconsistency-based multi-modal fusion network for fake news detection. Information Fusion 93, 1 (2023), 150–158.
Digital Library
[48]
Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, and Songlin Hu. 2019. Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In Proceedings of the 2019 IEEE International Conference on Data Mining. IEEE, 796–805.
[49]
Wenjia Zhang, Lin Gui, and Yulan He. 2021. Supervised contrastive learning for multimodal unreliable news detection in covid-19 pandemic. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 3637–3641.
Digital Library
[50]
Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021. Mining dual emotion for fake news detection. In Proceedings of the Web Conference 2021. 3465–3476.
Digital Library
[51]
Jiaqi Zheng, Xi Zhang, Sanchuan Guo, Quan Wang, Wenyu Zang, and Yongdong Zhang. 2022. MFAN: Multi-modal feature-enhanced attention networks for rumor detection. In IJCAI, Vol. 2022. 2413–2419.
[52]
Yangming Zhou, Yuzhou Yang, Qichao Ying, Zhenxing Qian, and Xinpeng Zhang. 2023. Multi-modal fake news detection on social media via multi-grained information fusion. In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval. 343–352.
Digital Library
[53]
Tong Zhu, Leida Li, Jufeng Yang, Sicheng Zhao, Hantao Liu, and Jiansheng Qian. 2022. Multimodal sentiment analysis with image-text interaction network. IEEE Transactions on Multimedia (2022).
Index Terms
GS2F: Multimodal Fake News Detection Utilizing Graph Structure and Guided Semantic Fusion
Applied computing
Document management and text processing
Computing methodologies
Artificial intelligence
Computer vision
Computer vision representations
Natural language processing
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Published In
ACM Transactions on Asian and Low-Resource Language Information Processing Volume 24, Issue 2
February 2025
225 pages
EISSN:2375-4702
DOI:10.1145/3696821
Issue’s Table of Contents
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 February 2025
Online AM: 16 December 2024
Accepted: 06 December 2024
Revised: 29 October 2024
Received: 17 March 2024
Published inTALLIPVolume 24, Issue 2
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Author Tags
- Fake news detection
- graph structure
- multi-view learning
- social media analysis
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Ministry of Education of Humanities and Social Science Project
- Philosophy and Social Sciences 14th Five-Year Plan Project of Guangdong Province
- Guangdong Basic and Applied Basic Research Foundation of China
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