Fetching the paper…
Reading the bibliography…
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
FaceForensics++: Learning to Detect Manipulated Facial Images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019 · 1901
Earlier work this paper cites.
CoAID: COVID-19 Healthcare Misinformation Dataset
Limeng Cui and Dongwon Lee. 2020 · 2006
Earlier work this paper cites.
Multimodal fusion for multimedia analysis: a survey
Pradeep K. Atrey, M. Anwar Hossain, Abdulmotaleb El Saddik, and M. Kankanhalli. 2010 · 2010
Earlier work this paper cites.
MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020a · 2011
Earlier work this paper cites.
Performance Evaluation of Early and Late Fusion Methods for Generic Semantics Indexing
Yuan Dong, Shan Gao, Kun Tao, Jiqing Liu, and Haila Wang. 2014 · 2014
Earlier work this paper cites.
Deep Multimodal Fusion: Combining Discrete Events and Continuous Signals. In Proceedings of the 16th International Conference on Multimodal Interaction (Istanbul, Turkey) (ICMI ’14) . Association for Computing Machinery, New York, NY, USA, 34–41
Héctor P. Martínez and Georgios N. Yannakakis. 2014 · 2014
Earlier work this paper cites.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
Earlier work this paper cites.
Microsoft COCO Captions: Data Collection and Evaluation Server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C. Lawrence Zitnick. 2015 · 2015
Earlier work this paper cites.
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Dana Lahat, T. Adalı, and Christian Jutten. 2015 · 2015
Earlier work this paper cites.
Recipe recognition with large multimodal food dataset. In 2015 IEEE International Conference on Multimedia Expo Workshops (ICMEW) . 1–6
Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, and Frédéric Precioso. 2015 · 2015
Earlier work this paper cites.
VQA: Visual Question Answering
Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra. 2017 · 2017
Earlier work this paper cites.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
Earlier work this paper cites.
This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In Proceedings of the international AAAI conference on web and social media , Vol. 11. 759–766
Benjamin Horne and Sibel Adali. 2017 · 2017
Earlier work this paper cites.
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs. In Proceedings of the 25th ACM International Conference on Multimedia (Mountain View, California, USA) (MM ’17) . Association for Computing Machinery, New York, NY, USA, 795–816
Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017 · 2017
Earlier work this paper cites.
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei. 2017 · 2017
Earlier work this paper cites.
Fake News Detection on Social Media: A Data Mining Perspective
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017 · 2017
Earlier work this paper cites.
Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining . SIAM, 99–107
Liang Wu, Jundong Li, Xia Hu, and Huan Liu. 2017 · 2017
Earlier work this paper cites.
A Brief Introduction to Weakly Supervised Learning
Zhi-Hua Zhou. 2017 · 2017
Earlier work this paper cites.
Detection and visualization of misleading content on Twitter
Christina Boididou, Symeon Papadopoulos, Markos Zampoglou, Lazaros Apostolidis, Olga Papadopoulou, and Yiannis Kompatsiaris. 2018 · 2018
Earlier work this paper cites.
Semi-supervised Content-Based Detection of Misinformation via Tensor Embeddings. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) . 322–325
Gisel Bastidas Guacho, Sara Abdali, Neil Shah, and Evangelos E. Papalexakis. 2018 · 2018
Earlier work this paper cites.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Danna Gurari, Qing Li, Abigale Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey Bigham. 2018 · 2018
Earlier work this paper cites.
Weakly Supervised Learning for Fake News Detection on Twitter. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) . 274–277
Stefan Helmstetter and Heiko Paulheim. 2018 · 2018
Earlier work this paper cites.
Fighting Fake News: Image Splice Detection via Learned Self-Consistency
Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei Efros. 2018 · 2018
Earlier work this paper cites.
False information on web and social media: A survey
Srijan Kumar and Neil Shah. 2018 · 2018
Earlier work this paper cites.
Understanding User Profiles on Social Media for Fake News Detection. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) . 430–435
Kai Shu, Suhang Wang, and Huan Liu. 2018 · 2018
Earlier work this paper cites.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 9 pages
Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018 · 2018
Earlier work this paper cites.
Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate
Liang Wu and Huan Liu. 2018 · 2018
Earlier work this paper cites.
Semi-Supervised Learning and Graph Neural Networks for Fake News Detection (ASONAM ’19) . Association for Computing Machinery, New York, NY, USA
Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, and Fragkiskos D. Malliaros. 2019 · 2019
Earlier work this paper cites.
Can Machines Learn to Detect Fake News? A Survey Focused on Social Media. In HICSS
Fernando Cardoso Durier da Silva, Rafael Vieira, and Ana Cristina Bicharra Garcia. 2019 · 2019
Earlier work this paper cites.
A MULTIMODAL APPROACH TO SARCASM DETECTION ON SOCIAL MEDIA
Dipto Das. 2019 · 2019
Earlier work this paper cites.
GQA: a new dataset for compositional question answering over real-world images
Drew Hudson and Christopher Manning. 2019 · 2019
Earlier work this paper cites.
Beyond news contents: the role of social context for fake news detection. In WSDM ’19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 312–320
S. Wang K. Shu, A. Sliva and H. Liu. 2019 · 2019
Earlier work this paper cites.
MVAE: Multimodal Variational Autoencoder for Fake News Detection. In The World Wide Web Conference (San Francisco, CA, USA) (WWW ’19) . Association for Computing Machinery, New York, NY, USA, 2915–2921
Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019 · 2019
Earlier work this paper cites.
Exploiting Multi-domain Visual Information for Fake News Detection. 518–527
Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, and Jintao Li. 2019 · 2019
Earlier work this paper cites.
Towards VQA Models that can Read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. 2019 · 2019
Cited alongside, same era.
SpotFake: A Multi-modal Framework for Fake News Detection. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) . 39–47
Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, and Shin’ichi Satoh. 2019 · 2019
Cited alongside, same era.
Network-Based Fake News Detection: A Pattern-Driven Approach
Xinyi Zhou and Reza Zafarani. 2019 · 2019
Cited alongside, same era.
HiJoD: Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition. In ECML/PKDD
Sara Abdali, Neil Shah, and Evangelos E. Papalexakis. 2020 · 2020
Cited alongside, same era.
Threats to Online Advertising and Countermeasures: A Technical Survey
Yegui Cai, George O. M. Yee, Yuan Xiang Gu, and Chung-Horng Lung. 2020 · 2020
Cited alongside, same era.
ARCNN framework for multimodal infodemic detection
Chahat Raj and Priyanka Meel. 2022 · 2021
Later among the works it cites.
Zero-shot text-to-image generation. In International Conference on Machine Learning . PMLR, 8821–8831
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Later among the works it cites.
Socially Aware Multimodal Deep Neural Networks for Fake News Classification. In 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) . 253–259
Saed Rezayi, Saber Soleymani, Hamid R. Arabnia, and Sheng Li. 2021 · 2021
Later among the works it cites.
SCATE: Shared Cross Attention Transformer Encoders for Multimodal Fake News Detection. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Virtual Event, Netherlands) (ASONAM ’21) . Association for Computing Machinery, New York, NY, USA, 399–406
Tanmay Sachan, Nikhil Pinnaparaju, Manish Gupta, and Vasudeva Varma. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation (KDD ’20) . Association for Computing Machinery, New York, NY, USA
Limeng Cui, Haeseung Seo, Maryam Tabar, Fenglong Ma, Suhang Wang, and Dongwon Lee. 2020 · 2020
Cited alongside, same era.
Image and Text fusion for UPMC Food-101 using BERT and CNNs. In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) . 1–6
Ignazio Gallo, Gianmarco Ria, Nicola Landro, and Riccardo La Grassa. 2020 · 2020
Cited alongside, same era.
Multimodal Multi-image Fake News Detection. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) . 647–654
Anastasia Giachanou, Guobiao Zhang, and Paolo Rosso. 2020 · 2020
Cited alongside, same era.
Captioning Images Taken by People Who Are Blind
Danna Gurari, Yinan Zhao, Meng Zhang, and Nilavra Bhattacharya. 2020 · 2020
Cited alongside, same era.
An ensemble machine learning approach through effective feature extraction to classify fake news
Saqib Hakak, Mamoun Alazab, Suleman Khan, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, and Wazir Zada Khan. 2021 · 2020
Cited alongside, same era.
Deep learning for misinformation detection on online social networks: a survey and new perspectives
Md Rafiqul Islam, Shaowu Liu, Wang Xianzhi, and Guandong Xu. 2020 · 2020
Cited alongside, same era.
NewsBag: A multimodal benchmark dataset for fake news detection
S. Jindal, R. Sood, Richa Singh, Mayank Vatsa, and T. Chakraborty. 2020 · 2020
Cited alongside, same era.
A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. 899–908
Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. 2021 · 2021
Later among the works it cites.
Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection
Amila Silva, Yi Han, Ling Luo, Shanika Karunasekera, and Christopher Leckie. 2021a · 2021
Later among the works it cites.
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
Amila Silva, Ling Luo, Shanika Karunasekera, and Christopher Leckie. 2021b · 2021
Later among the works it cites.
Inter-Modality Discordance for Multimodal Fake News Detection. In ACM Multimedia Asia (Gold Coast, Australia) (MMAsia ’21) . Association for Computing Machinery, New York, NY, USA, Article 33, 7 pages
Shivangi Singhal, Mudit Dhawan, Rajiv Ratn Shah, and Ponnurangam Kumaraguru. 2021 · 2021
Later among the works it cites.
A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks
Chenguang Song, Nianwen Ning, Yunlei Zhang, and Bin Wu. 2021a · 2021
Later among the works it cites.
Temporally evolving graph neural network for fake news detection
Chenguang Song, Kai Shu, and Bin Wu. 2021b · 2021
Later among the works it cites.
Temporally evolving graph neural network for fake news detection
Chenguang Song, Kai Shu, and Bin Wu. 2021c · 2021
Later among the works it cites.
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, and Jing Gao. 2021a · 2021
Later among the works it cites.
Detecting fake news by exploring the consistency of multimodal data
Junxiao Xue, Yabo Wang, Yichen Tian, Yafei Li, Lei Shi, and Lin Wei. 2021 · 2021
Later among the works it cites.
Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
Later among the works it cites.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
Closest in time.
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1530–1549
Giscard Biamby, Grace Luo, Trevor Darrell, and Anna Rohrbach. 2022 · 2022
Closest in time.
Effective fake news video detection using domain knowledge and multimodal data fusion on youtube
Hyewon Choi and Youngjoong Ko. 2022 · 2022
Closest in time.
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection
Mudit Dhawan, Shakshi Sharma, Aditya Kadam, Rajesh Sharma, and Ponnurangam Kumaraguru. 2022 · 2022
Closest in time.
AENeT: an attention-enabled neural architecture for fake news detection using contextual features
Vidit Jain, Rohit Kaliyar, Anurag Goswami, Pratik Narang, and Yashvardhan Sharma. 2022 · 2022
Closest in time.
Fake news detection via knowledgeable prompt learning
Gongyao Jiang, Shuang Liu, Yu Zhao, Yueheng Sun, and Meishan Zhang. 2022 · 2022
Closest in time.
Mumin: A large-scale multilingual multimodal fact-checked misinformation social network dataset. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 3141–3153
Dan S Nielsen and Ryan McConville. 2022 · 2022
Closest in time.
Multimodal fake news detection
Isabel Segura-Bedmar and Santiago Alonso-Bartolome. 2022 · 2022
Closest in time.
FMFN: Fine-Grained Multimodal Fusion Networks for Fake News Detection
Jingzi Wang, Hongyan Mao, and Hongwei Li. 2022a · 2022
Closest in time.
N24News: A New Dataset for Multimodal News Classification. In 13th International Conference on Language Resources and Evaluation Conference, LREC 2022 . European Language Resources Association (ELRA), 6768–6775
Zhen Wang, Xu Shan, Xiangxie Zhang, and Jie Yang. 2022b · 2022
Closest in time.
MDMN: Multi-task and Domain Adaptation based Multi-modal Network for early rumor detection
Honghao Zhou, Tinghuai Ma, Huan Rong, Yurong Qian, Yuan Tian, and Najla Al-Nabhan. 2022 · 2022
Closest in time.
Yuhui Zuo, Wei Zhu, and Guoyong GUET Cai. 2022 · 2022
Closest in time.
Multimodal Propaganda Detection Via Anti-Persuasion Prompt enhanced contrastive learning. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 1–5
Jian Cui, Lin Li, Xin Zhang, and Jingling Yuan. 2023 · 2023
Closest in time.
Few-shot fake news detection via prompt-based tuning
Wang Gao, Mingyuan Ni, Hongtao Deng, Xun Zhu, Peng Zeng, and Xi Hu. 2023 · 2023
Closest in time.
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
Ye Jiang, Xiaomin Yu, Yimin Wang, Xiaoman Xu, Xingyi Song, and Diana Maynard. 2023 · 2023
Closest in time.
Zero-shot rumor detection with propagation structure via prompt learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 5213–5221
Hongzhan Lin, Pengyao Yi, Jing Ma, Haiyun Jiang, Ziyang Luo, Shuming Shi, and Ruifang Liu. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Link-Context Learning for Multimodal LLMs
Yan Tai, Weichen Fan, Zhao Zhang, Feng Zhu, Rui Zhao, and Ziwei Liu. 2023 · 2023
Closest in time.
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters. In Proceedings of the ACM Web Conference 2023 . 1743–1753
Lin Tian, Xiuzhen Zhang, and Jey Han Lau. 2023 · 2023
Closest in time.
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang, Lanyu Shang, and Dong Wang. 2023 · 2023
Closest in time.