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A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning [article]

Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao
2022 arXiv   pre-print
Recent studies reveal that rumor detection and stance detection are two different but relevant tasks which can jointly enhance each other, e.g., rumors can be debunked by cross-checking the stances conveyed  ...  by their relevant microblog posts, and stances are also conditioned on the nature of the rumor.  ...  . (6) MT-GRU [34] : A multi-task learning approach to jointly detect rumors and stances by capturing both shared and task-specific features. (7) MTL2 [20] : A sequential approach sharing a LSTM layer  ... 
arXiv:2204.02626v2 fatcat:xtjmy5j2lfhcfmbauxmi2l6ppa

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences.  ...  Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels  ...  . (6) MT-GRU [34] : A multi-task learning approach to jointly detect rumors and stances by capturing both shared and task-specific features. (7) MTL2 [20] : A sequential approach sharing a LSTM layer  ... 
doi:10.1145/3477495.3531930 fatcat:6uniyvsljzhhrkquyss6sxo65e

SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media [article]

Chunyuan Yuan, Wanhui Qian, Qianwen Ma, Wei Zhou, Songlin Hu
2021 arXiv   pre-print
Then, we propose a novel Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality labeled stance data for model training and rumor detection.  ...  Both the stance selection and rumor detection tasks are optimized simultaneously to promote both tasks mutually. We conduct experiments on two commonly used real-world datasets.  ...  This research is supported in part by the National Key Research and Development Program of China under Grant 2018YFC0806900.  ... 
arXiv:2105.04098v1 fatcat:qvtvru4qyjax7kk7p5cphnp6iy

Rumor Detection on Social Media: Datasets, Methods and Opportunities [article]

Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu
2019 arXiv   pre-print
Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques.  ...  This paper gives an overview of the recent studies in the rumor detection field.  ...  learning approaches that jointly learn stance detection and rumor detection models.  ... 
arXiv:1911.07199v1 fatcat:h4fk3dyodjgyvffwuo6q5d2tnm

Rumor Stance Classification in Online Social Networks: A Survey on the State-of-the-Art, Prospects, and Future Challenges [article]

Sarina Jami, Iman Sahebi, Mohammad M. Sabermahani, Seyed P. Shariatpanahi, Aresh Dadlani, Behrouz Maham
2022 arXiv   pre-print
One such drawback is the spread of rumors facilitated by social media platforms which may provoke doubt and fear upon people.  ...  One aspect of such studies focuses on rumor stance classification, which concerns the task of utilizing users' viewpoints about a rumorous post to better predict the veracity of a rumor.  ...  In this section, we study two approaches in rumor stance classification which utilize neural networks, i.e., single-task learning and multi-task learning. 1) Single-Task Learning: Deep learning has grown  ... 
arXiv:2208.01721v1 fatcat:k4tstotwarhafeivshqqkjrbdy

event

Quanzhi Li, Qiong Zhang, Luo Si
2019 Proceedings of the 13th International Workshop on Semantic Evaluation  
This track has two tasks: Task A is to determine a user's stance towards the source rumor, and Task B is to detect the veracity of the rumor: true, false or unverified.  ...  Our system is ranked 1 st place in the rumor verification task by both the macro F1 measure and the RMSE measure.  ...  Recent works have employed multi-task learning approaches to jointly learn stance detection and veracity prediction, in order to improve classification accuracy by utilizing the interdependence between  ... 
doi:10.18653/v1/s19-2148 dblp:conf/semeval/LiZS19 fatcat:k42xdskknvbjhceq33afb5rmqm

Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity [article]

Anant Khandelwal
2020 arXiv   pre-print
In this paper,we propose a multi-task learning framework for jointly predicting rumor stance and veracity on the dataset released at SemEval 2019 RumorEval: Determining rumor veracity and support for rumors  ...  Experimental results on SemEval 2019 Task 7 dataset show that our method outperforms previous methods on both rumor stance classification and veracity prediction  ...  PROPOSED METHOD We proposed the multi-task learning framework for jointly predicting rumor stance and veracity.  ... 
arXiv:2007.07803v1 fatcat:gisdkfatt5gfjpatnnvaa3arzu

Research status of deep learning methods for rumor detection

Li Tan, Ge Wang, Feiyang Jia, Xiaofeng Lian
2022 Multimedia tools and applications  
Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods  ...  And compare the advantages of different methods to detect rumors.  ...  Ma, Gao, and Wong (2018a) first proposed a multi-task rumor detection method based on stance detection task.  ... 
doi:10.1007/s11042-022-12800-8 pmid:35469150 pmcid:PMC9022167 fatcat:h5vjukpkyzdhnjhikgtpj347e4

The Future of Misinformation Detection: New Perspectives and Trends [article]

Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu
2019 arXiv   pre-print
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion  ...  , and explanatory detection.  ...  [67] consider that rumor detection is highly correlated with the stance classification task, and thus propose a neural multi-task learning framework for be er detection.  ... 
arXiv:1909.03654v1 fatcat:34h2os2pzrbm3kqluk5uajtr6i

Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection

Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection.  ...  Recently, neural networks based on multitask learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve  ...  Acknowledgments The research work is supported by "the World-Class Universities(Disciplines) and the Characteristic Development Guidance Funds for the Central Universities"(PY3A022), Shenzhen Science and  ... 
doi:10.18653/v1/d19-1471 dblp:conf/emnlp/WuRJNS19 fatcat:x4ogedygirdc3jbc7plmjepuye

Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

Jing Ma, Wei Gao, Kam-Fai Wong
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation  ...  Automatic rumor detection is technically very challenging.  ...  Acknowledgment This work is partly supported by Innovation and Technology Fund (ITF) Project No. 6904333, and General Research Fund (GRF) Project No. 14232816 (12183516).  ... 
doi:10.18653/v1/p18-1184 dblp:conf/acl/WongGM18 fatcat:nxj3ahb2jferbgr2q23qelmp3i

Early Detection of Rumours on Twitter via Stance Transfer Learning [chapter]

Lin Tian, Xiuzhen Zhang, Yan Wang, Huan Liu
2020 Lecture Notes in Computer Science  
Specifically we propose convolutional neural network (CNN) CNN and BERT neural network language models to learn attitude representation for user comments without human annotation via transfer learning  ...  We further propose CNN-BiLSTM-and BERT-based deep neural models to combine attitude representation and content representation for early rumour detection.  ...  This research is supported in part by the Australian Research Council Discovery Project DP200101441.  ... 
doi:10.1007/978-3-030-45439-5_38 fatcat:45kaddfe7vbfncjcwnod2rxh24

RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media [article]

Jie Gao, Sooji Han, Xingyi Song, Fabio Ciravegna
2020 arXiv   pre-print
We apply multi-layered attention models to jointly learn attentive context embeddings over multiple context inputs.  ...  They are not appropriate to detect rumor sources in the very early stages, before an event unfolds and becomes widespread. In this paper, we address the task of ERD at the message level.  ...  ., 2018a) proposes a GRU-based, multi-task learning architecture which unifies both stances and rumor detection.  ... 
arXiv:2002.12683v2 fatcat:dx4is5w6yzfk7mcbzf72kxcza4

Deep Learning Based Rumor Detection on Microblogging Platforms: A Systematic Review

Mohammed Al-Sarem, Wadii Boulila, Muna Al-Harby, Junaid Qadir, Abdullah Alsaeedi
2019 IEEE Access  
In this paper, we conducted a systematic literature review for rumor detection using deep neural network approaches.  ...  INDEX TERMS Deep learning, rumor detection, systematic review, Twitter analysis.  ...  [74] , there are four types of rumor classification tasks, namely, rumor detection, rumor tracking, stance detection, and veracity.  ... 
doi:10.1109/access.2019.2947855 fatcat:dltss2k2yjgpzcjwubbmhfrbbu

Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection [article]

Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun
2019 arXiv   pre-print
Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve  ...  In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection.  ...  Acknowledgments The research work is supported by "the World-Class Universities(Disciplines) and the Characteristic Development Guidance Funds for the Central Universities"(PY3A022), Shenzhen Science and  ... 
arXiv:1909.01720v1 fatcat:vjefust43fcbreatac6sw44imu
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