Fake News Quick Detection on Dynamic Heterogeneous Information Networks
- URL: http://arxiv.org/abs/2205.07039v1
- Date: Sat, 14 May 2022 11:23:25 GMT
- Title: Fake News Quick Detection on Dynamic Heterogeneous Information Networks
- Authors: Jin Ho Go, Alina Sari, Jiaojiao Jiang, Shuiqiao Yang, Sanjay Jha
- Abstract summary: 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.
- Score: 3.599616699656401
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The spread of fake news has caused great harm to society in recent years. So
the quick detection of fake news has become an important task. Some current
detection methods often model news articles and other related components as a
static heterogeneous information network (HIN) and use expensive
message-passing algorithms. However, in the real-world, quickly identifying
fake news is of great significance and the network may vary over time in terms
of dynamic nodes and edges. Therefore, in this paper, we propose a novel
Dynamic Heterogeneous Graph Neural Network (DHGNN) for fake news quick
detection. More specifically, we first implement BERT and fine-tuned BERT to
get a semantic representation of the news article contents and author profiles
and convert it into graph data. Then, we construct the heterogeneous
news-author graph to reflect contextual information and relationships.
Additionally, we adapt ideas from personalized PageRank propagation and dynamic
propagation to heterogeneous networks in order to reduce the time complexity of
back-propagating through many nodes during training. Experiments on three
real-world fake news datasets show that DHGNN can outperform other GNN-based
models in terms of both effectiveness and efficiency.
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) - Nothing Stands Alone: Relational Fake News Detection with Hypergraph
Neural Networks [49.29141811578359]
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.
arXiv Detail & Related papers (2022-12-24T00:19:32Z) - 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) - Evidential Temporal-aware Graph-based Social Event Detection via
Dempster-Shafer Theory [76.4580340399321]
We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network.
We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively.
Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
arXiv Detail & Related papers (2022-05-24T16:22:40Z) - A comparative analysis of Graph Neural Networks and commonly used
machine learning algorithms on fake news detection [0.0]
Low cost, simple accessibility via social platforms, and a plethora of low-budget online news sources are some of the factors that contribute to the spread of false news.
Most of the existing fake news detection algorithms are solely focused on the news content only.
engaged users prior posts or social activities provide a wealth of information about their views on news and have significant ability to improve fake news identification.
arXiv Detail & Related papers (2022-03-26T18:40:03Z) - 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) - Adversarial Active Learning based Heterogeneous Graph Neural Network for
Fake News Detection [18.847254074201953]
We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN)
AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models.
arXiv Detail & Related papers (2021-01-27T05:05:25Z) - Graph Neural Networks with Continual Learning for Fake News Detection
from Social Media [18.928184473686567]
We use graph neural networks (GNNs) to differentiate between the propagation patterns of fake and real news on social media.
Without relying on any text information, we show that GNNs can achieve comparable or superior performance without any text information.
We propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.
arXiv Detail & Related papers (2020-07-07T10:04:50Z) - Graph Enhanced Representation Learning for News Recommendation [85.3295446374509]
We propose a news recommendation method which can enhance the representation learning of users and news.
In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors.
arXiv Detail & Related papers (2020-03-31T15:27:31Z) - Fake News Detection on News-Oriented Heterogeneous Information Networks
through Hierarchical Graph Attention [12.250335118888891]
We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT)
HGAT uses a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes.
Experiments on two real-world fake news datasets show that HGAT can outperform text-based models and other network-based models.
arXiv Detail & Related papers (2020-02-05T19:09:13Z)
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.