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A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
[article]
2022
arXiv
pre-print
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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
2022
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2019
Proceedings of the 13th International Workshop on Semantic Evaluation
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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]
2020
arXiv
pre-print
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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
2022
Multimedia tools and applications
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2022 pre-print arXiv:2204.11540v1 fatcat:4oeelrf3gnbmhnr7lgmut2ca4y
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]
2019
arXiv
pre-print
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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
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)
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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
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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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]
2020
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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
2019
IEEE Access
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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]
2019
arXiv
pre-print
Preserved Fulltext
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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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