Nothing Stands Alone: Relational Fake News Detection with Hypergraph
Neural Networks
- URL: http://arxiv.org/abs/2212.12621v1
- Date: Sat, 24 Dec 2022 00:19:32 GMT
- Title: Nothing Stands Alone: Relational Fake News Detection with Hypergraph
Neural Networks
- Authors: Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu
- Abstract summary: We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism.
Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
- Score: 49.29141811578359
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nowadays, fake news easily propagates through online social networks and
becomes a grand threat to individuals and society. Assessing the authenticity
of news is challenging due to its elaborately fabricated contents, making it
difficult to obtain large-scale annotations for fake news data. Due to such
data scarcity issues, detecting fake news tends to fail and overfit in the
supervised setting. Recently, graph neural networks (GNNs) have been adopted to
leverage the richer relational information among both labeled and unlabeled
instances. Despite their promising results, they are inherently focused on
pairwise relations between news, which can limit the expressive power for
capturing fake news that spreads in a group-level. For example, detecting fake
news can be more effective when we better understand relations between news
pieces shared among susceptible users. To address those issues, we propose to
leverage a hypergraph to represent group-wise interaction among news, while
focusing on important news relations with its dual-level attention mechanism.
Experiments based on two benchmark datasets show that our approach yields
remarkable performance and maintains the high performance even with a small
subset of labeled news data.
Related papers
- Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news.
Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
arXiv Detail & Related papers (2023-11-02T08:39:45Z) - Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News
Detection [50.07850264495737]
"Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection.
We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
arXiv Detail & Related papers (2023-09-28T13:19:43Z) - Mining User-aware Multi-Relations for Fake News Detection in Large Scale
Online Social Networks [25.369320307526362]
credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news.
We construct a dual-layer graph to extract multiple relations of news and users in social networks to derive rich information for detecting fake news.
We propose a fake news detection model named Us-DeFake, which learns the propagation features of news in the news layer and the interaction features of users in the user layer.
arXiv Detail & Related papers (2022-12-21T05:30:35Z) - Modelling Social Context for Fake News Detection: A Graph Neural Network
Based Approach [0.39146761527401425]
Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystems reliability.
This paper has analyzed the social context of fake news detection with a hybrid graph neural network based approach.
arXiv Detail & Related papers (2022-07-27T12:58:33Z) - Fake News Quick Detection on Dynamic Heterogeneous Information Networks [3.599616699656401]
We propose a novel Dynamic Heterogeneous Graph Neural Network (DHGNN) for fake news quick detection.
We first implement BERT and fine-tuned BERT to get a semantic representation of the news article contents and author profiles.
Then, we construct the heterogeneous news-author graph to reflect contextual information and relationships.
arXiv Detail & Related papers (2022-05-14T11:23:25Z) - 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) - An Event Correlation Filtering Method for Fake News Detection [0.0]
Existing deep learning models have achieved great progress to tackle the problem of fake news detection.
To improve the detection performance of fake news, we take advantage of the event correlations of news.
ECFM is proposed to integrate them to detect fake news in an event correlation filtering manner.
arXiv Detail & Related papers (2020-12-10T07:31:07Z) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Leveraging Multi-Source Weak Social Supervision for Early Detection of
Fake News [67.53424807783414]
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation.
We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances.
Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
arXiv Detail & Related papers (2020-04-03T18:26:33Z) - Weak Supervision for Fake News Detection via Reinforcement Learning [34.448503443582396]
We propose a weakly-supervised fake news detection framework, i.e., WeFEND.
The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector.
We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports.
arXiv Detail & Related papers (2019-12-28T21:20:25Z)
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.