MDFEND: Multi-domain Fake News Detection
- URL: http://arxiv.org/abs/2201.00987v1
- Date: Tue, 4 Jan 2022 05:28:25 GMT
- Title: MDFEND: Multi-domain Fake News Detection
- Authors: Qiong Nan, Juan Cao, Yongchun Zhu, Yanyan Wang, Jintao Li
- Abstract summary: We propose an effective Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to aggregate multiple representations extracted by a mixture of experts.
The experiments show that MDFEND can significantly improve the performance of multi-domain fake news detection.
- Score: 15.767582764441627
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fake news spread widely on social media in various domains, which lead to
real-world threats in many aspects like politics, disasters, and finance. Most
existing approaches focus on single-domain fake news detection (SFND), which
leads to unsatisfying performance when these methods are applied to
multi-domain fake news detection. As an emerging field, multi-domain fake news
detection (MFND) is increasingly attracting attention. However, data
distributions, such as word frequency and propagation patterns, vary from
domain to domain, namely domain shift. Facing the challenge of serious domain
shift, existing fake news detection techniques perform poorly for multi-domain
scenarios. Therefore, it is demanding to design a specialized model for MFND.
In this paper, we first design a benchmark of fake news dataset for MFND with
domain label annotated, namely Weibo21, which consists of 4,488 fake news and
4,640 real news from 9 different domains. We further propose an effective
Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to
aggregate multiple representations extracted by a mixture of experts. The
experiments show that MDFEND can significantly improve the performance of
multi-domain fake news detection. Our dataset and code are available at
https://github.com/kennqiang/MDFEND-Weibo21.
Related papers
- FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection [54.37159298632628]
FineFake is a multi-domain knowledge-enhanced benchmark for fake news detection.
FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms.
The entire FineFake project is publicly accessible as an open-source repository.
arXiv Detail & Related papers (2024-03-30T14:39:09Z) - Robust Domain Misinformation Detection via Multi-modal Feature Alignment [49.89164555394584]
We propose a robust domain and cross-modal approach for multi-modal misinformation detection.
It reduces the domain shift by aligning the joint distribution of textual and visual modalities.
We also propose a framework that simultaneously considers application scenarios of domain generalization.
arXiv Detail & Related papers (2023-11-24T07:06:16Z) - Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak
Signals [19.22829945777267]
This work proposes an effective framework for unsupervised fake news detection, which first embeds the knowledge available in four modalities in news records.
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
We trained the proposed unsupervised framework using LUND-COVID to exploit the potential of large datasets.
arXiv Detail & Related papers (2023-05-18T23:49:31Z) - Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
Multiverse is a new feature based on multilingual evidence that can be used for fake news detection.
The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
arXiv Detail & Related papers (2022-11-25T18:24:17Z) - Improving Fake News Detection of Influential Domain via Domain- and
Instance-Level Transfer [16.886024206337257]
We propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND)
DITFEND could improve the performance of specific target domains.
Online experiments show that it brings additional improvements over the base models in a real-world scenario.
arXiv Detail & Related papers (2022-09-19T10:21:13Z) - Memory-Guided Multi-View Multi-Domain Fake News Detection [39.035462224569166]
We propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$3$FEND) to address these two challenges.
Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels.
With enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains.
arXiv Detail & Related papers (2022-06-26T07:09:23Z) - Domain Adaptive Fake News Detection via Reinforcement Learning [34.95213747705498]
We introduce a novel reinforcement learning-based model called REAL-FND to detect fake news.
Experiments on real-world datasets illustrate the effectiveness of the proposed model.
arXiv Detail & Related papers (2022-02-16T16:05:37Z) - User Preference-aware Fake News Detection [61.86175081368782]
Existing fake news detection algorithms focus on mining news content for deceptive signals.
We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
arXiv Detail & Related papers (2021-04-25T21:19:24Z) - Embracing Domain Differences in Fake News: Cross-domain Fake News
Detection using Multi-modal Data [18.66426327152407]
We propose a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains.
Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets.
arXiv Detail & Related papers (2021-02-11T23:31:14Z) - A Deep Learning Approach for Automatic Detection of Fake News [47.00462375817434]
We propose two models based on deep learning for solving fake news detection problem in online news contents of multiple domains.
We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection.
arXiv Detail & Related papers (2020-05-11T09:07:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.