research-article

Hierarchical Semantic Enhancement Network for Multimodal Fake News Detection

Authors:

Zheng-Jun ZhaAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 3424 - 3433

https://doi.org/10.1145/3581783.3612423

Published: 27 October 2023 Publication History

Abstract

The explosion of multimodal fake news content on social media has sparked widespread concern. Existing multimodal fake news detection methods have made significant contributions to the development of this field, but fail to adequately exploit the potential semantic information of images and ignore the noise embedded in news entities, which severely limits the performance of the models. In this paper, we propose a novel Hierarchical Semantic Enhancement Network (HSEN) for multimodal fake news detection by learning text-related image semantic and precise news high-order knowledge semantic information. Specifically, to complement the image semantic information, HSEN utilizes textual entities as the prompt subject vocabulary and applies reinforcement learning to discover the optimal prompt format for generating image captions specific to the corresponding textual entities, which contain multi-level cross-modal correlation information. Moreover, HSEN extracts visual and textual entities from image and text, and identifies additional visual entities from image captions to extend image semantic knowledge. Based on that, HSEN exploits an adaptive hard attention mechanism to automatically select strongly related news entities and remove irrelevant noise entities to obtain precise high-order knowledge semantic information, while generating attention mask for guiding cross-modal knowledge interaction. Extensive experiments show that our method outperforms state-of-the-art methods.

References

[1]

Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6077--6086.

[2]

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 1247--1250.

Digital Library

[3]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, Vol. 26 (2013).

[4]

Shizhe Chen, Qin Jin, Peng Wang, and Qi Wu. 2020. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9962--9971.

[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]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248--255.

[7]

Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P Xing, and Zhiting Hu. 2022. Rlprompt: Optimizing discrete text prompts with reinforcement learning. arXiv preprint arXiv:2205.12548 (2022).

[8]

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

[9]

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

[10]

Song Feng, Ritwik Banerjee, and Yejin Choi. 2012. Syntactic stylometry for deception detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 171--175.

Digital Library

[11]

Ross Girshick. 2015. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision. 1440--1448.

Digital Library

[12]

Han Guo, Bowen Tan, Zhengzhong Liu, Eric P Xing, and Zhiting Hu. 2021. Text generation with efficient (soft) q-learning. arXiv preprint arXiv:2106.07704 (2021).

[13]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[14]

Lun Huang, Wenmin Wang, Jie Chen, and Xiao-Yong Wei. 2019. Attention on attention for image captioning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4634--4643.

[15]

Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015).

[16]

Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros. 2018. Fighting fake news: Image splice detection via learned self-consistency. In Proceedings of the European Conference on Computer Vision (ECCV). 101--117.

Digital Library

[17]

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

Digital Library

[18]

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.

Digital Library

[19]

Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, and Jing Jiang. 2020. Interpretable rumor detection in microblogs by attending to user interactions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 8783--8790.

[20]

Dong Li, Jiaying Zhu, Menglu Wang, Jiawei Liu, Xueyang Fu, and Zheng-Jun Zha. 2023. Edge-Aware Regional Message Passing Controller for Image Forgery Localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8222--8232.

[21]

Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022a. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning. PMLR, 12888--12900.

[22]

Peiguang Li, Xian Sun, Hongfeng Yu, Yu Tian, Fanglong Yao, and Guangluan Xu. 2022b. Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection., Vol. 24 (2022).

Digital Library

[23]

Yang Liu and Yi-Fang Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.

[24]

Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 2015. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 1751--1754.

Digital Library

[25]

Rahul Mishra. 2020. Fake news detection using higher-order user to user mutual-attention progression in propagation paths. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops. 652--653.

[26]

Kartik Narayan, Harsh Agarwal, Surbhi Mittal, Kartik Thakral, Suman Kundu, Mayank Vatsa, and Richa Singh. 2022. DeSI: Deepfake Source Identifier for Social Media. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2858--2867.

[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]

Feng Qian, Chengyue Gong, Karishma Sharma, and Yan Liu. 2018. Neural User Response Generator: Fake News Detection with Collective User Intelligence. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vol. 18. 3834--3840.

[29]

Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018).

[30]

Zhihua Shang, Hongtao Xie, Zhengjun Zha, Lingyun Yu, Yan Li, and Yongdong Zhang. 2021. PRRNet: Pixel-Region relation network for face forgery detection. Pattern Recognition, Vol. 116 (2021), 107950.

Digital Library

[31]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

[32]

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.

[33]

Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, and Shin'ichi Satoh. 2019a. Spotfake: A multi-modal framework for fake news detection. In 2019 IEEE fifth International Conference on Multimedia Big Data (BigMM). IEEE, 39--47.

[34]

Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, and Shin'ichi Satoh. 2019b. SpotFake: A Multi-modal Framework for Fake News Detection. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM). 39--47. https://doi.org/10.1109/BigMM.2019.00-44

[35]

Zeliang Song, Xiaofei Zhou, Zhendong Mao, and Jianlong Tan. 2021. Image captioning with context-aware auxiliary guidance. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 2584--2592.

[36]

Mengzhu Sun, Xi Zhang, Jianqiang Ma, and Yazheng Liu. 2021. Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021. 1412--1423.

[37]

Mengzhu Sun, Xi Zhang, Jiaqi Zheng, and Guixiang Ma. 2022. Ddgcn: Dual dynamic graph convolutional networks for rumor detection on social media. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 4611--4619.

[38]

Yu-Wun Tseng, Hui-Kuo Yang, Wei-Yao Wang, and Wen-Chih Peng. 2022. KAHAN: Knowledge-Aware Hierarchical Attention Network for Fake News detection on Social Media. In Companion Proceedings of the Web Conference 2022. 868--875.

Digital Library

[39]

Menglu Wang, Xueyang Fu, Jiawei Liu, and Zheng-Jun Zha. 2022a. Jpeg compress-ion-aware image forgery localization. In Proceedings of the 30th ACM International Conference on Multimedia. 5871--5879.

[40]

Ning Wang, Jiahao Xie, Jihao Wu, Mingbo Jia, and Linlin Li. 2022b. Controllable Image Captioning via Prompting. arXiv preprint arXiv:2212.01803 (2022).

[41]

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

[42]

Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, and Jing Gao. 2021. Multimodal emergent fake news detection via meta neural process networks. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3708--3716.

Digital Library

[43]

Youze Wang, Shengsheng Qian, Jun Hu, Quan Fang, and Changsheng Xu. 2020. Fake news detection via knowledge-driven multimodal graph convolutional networks. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 540--547.

Digital Library

[44]

Yiyu Wang, Jungang Xu, and Yingfei Sun. 2022c. End-to-end transformer based model for image captioning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 2585--2594.

[45]

Ke Wu, Song Yang, and Kenny Q Zhu. 2015. False rumors detection on sina weibo by propagation structures. In 2015 IEEE 31st International Conference on Data Engineering. IEEE, 651--662.

[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]

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning. PMLR, 2048--2057.

[48]

Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, and Xi Zhang. 2021. Rumor detection on social media with graph structured adversarial learning. In Proceedings of the twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence. 1417--1423.

[49]

Qichao Ying, Xiaoxiao Hu, Yangming Zhou, Zhenxing Qian, Dan Zeng, and Shiming Ge. 2022. Bootstrapping Multi-view Representations for Fake News Detection. arxiv: 2206.05741 [cs.CV]

[50]

Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917 (2022).

[51]

Huaiwen Zhang, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2021b. Multi-modal meta multi-task learning for social media rumor detection. IEEE Transactions on Multimedia, Vol. 24 (2021), 1449--1459.

Digital Library

[52]

Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. 2021a. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5579--5588.

[53]

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 Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. 2413--2419.

[54]

Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020.: Similarity-Aware Multi-modal Fake News Detection. In Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings, Part II. Springer, 354--367.

Digital Library

[55]

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

Cited By

Ye YChen M(2025)Fake News Detection by Granularity Enhanced Multimodal High-Order Interaction NetworkAdvanced Manufacturing and Automation XIV10.1007/978-981-96-2625-0_21(164-171)Online publication date: 15-Feb-2025
https://doi.org/10.1007/978-981-96-2625-0_21
Zhang QLiu JZhang FXie JZha ZLarson K(2024)Natural language-centered inference network for multi-modal fake news detectionProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/281(2542-2550)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/281
Zhang FLiu JXie JZhang QXu YZha ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-CheckingProceedings of the ACM Web Conference 202410.1145/3589334.3645455(2429-2440)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645455

Index Terms

Hierarchical Semantic Enhancement Network for Multimodal Fake News Detection
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
  2. World Wide Web
    1. Web applications
      1. Social networks

Recommendations

Cross-modal Ambiguity Learning for Multimodal Fake News Detection
WWW '22: Proceedings of the ACM Web Conference 2022

Cross-modal learning is essential to enable accurate fake news detection due to the fast-growing multimodal contents in online social communities. A fundamental challenge of multimodal fake news detection lies in the inherent ambiguity across different ...
MVAE: Multimodal Variational Autoencoder for Fake News Detection
WWW '19: The World Wide Web Conference

In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound ...
MCAN: multimodal cross-aware network for fake news detection by extracting semantic-physical feature consistency
Abstract
Social platforms are vital for information dissemination but also contribute to the spread of fake news, causing confusion and misinformation. To combat this, advancements in detection technology are crucial, particularly for posts that combine ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key R&D Program of China under Grant
National Natural Science Foundation of China (NSFC) under Grants

Conference

MM '23

Sponsor:

SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
489
Total Downloads

Downloads (Last 12 months)250
Downloads (Last 6 weeks)21

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ye YChen M(2025)Fake News Detection by Granularity Enhanced Multimodal High-Order Interaction NetworkAdvanced Manufacturing and Automation XIV10.1007/978-981-96-2625-0_21(164-171)Online publication date: 15-Feb-2025
https://doi.org/10.1007/978-981-96-2625-0_21
Zhang QLiu JZhang FXie JZha ZLarson K(2024)Natural language-centered inference network for multi-modal fake news detectionProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/281(2542-2550)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/281
Zhang FLiu JXie JZhang QXu YZha ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-CheckingProceedings of the ACM Web Conference 202410.1145/3589334.3645455(2429-2440)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645455

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten