FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
@article{Nguyen2020FANGLS, title={FANG: Leveraging Social Context for Fake News Detection Using Graph Representation}, author={Van-Hoang Nguyen and Kazunari Sugiyama and Preslav Nakov and Min-Yen Kan}, journal={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management}, year={2020} }
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph. Our experimental results show that FANG is better at capturing…
Figures and Tables from this paper
76 Citations
Graph-based Modeling of Online Communities for Fake News Detection
- Computer ScienceArXiv
- 2020
This work proposes a novel social context-aware fake news detection framework, SAFER, based on graph neural networks (GNNs), and introduces novel methods based on relational and hyperbolic GNNs, which have not been previously used for user or community modeling within NLP.
Can We Spot the "Fake News" Before It Was Even Written?
- Computer Science
- 2020
Media profiles are developed that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame ofReporting, and stance with respect to various claims and topics in the Tanbih news aggregator.
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks
- Computer ScienceACL
- 2022
Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news,…
Fact-Checking, Fake News, Propaganda, Media Bias, and the COVID-19 Infodemic
- Computer ScienceWSDM
- 2022
This work offers an overview of the emerging and inter-connected research areas of fact-checking, disinformation, "fake news'', propaganda, and media bias detection, and the ongoing COVID-19 infodemic.
Fake news detection based on news content and social contexts: a transformer-based approach
- Computer ScienceInternational journal of data science and analytics
- 2022
Experimental results on real-world data show that the proposed fake news detection framework can detect fake news with higher accuracy within a few minutes after it propagates (early detection) than the baselines.
A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations
- Computer ScienceArXiv
- 2021
A graph data structure that combines underlying users’ relational event information, as well as semantic and topical modeling, is presented, which shows a consistent 3%-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered.
Fake News, Disinformation, Propaganda, and Media Bias
- Computer ScienceCIKM
- 2021
The research community responded to the issue, proposing a number of inter-connected research directions such as fact-checking, disinformation, misinformation, fake news, propaganda, and media bias detection, which are of interest to human fact-checkers and journalists.
Hetero-SCAN: Towards Social Context Aware Fake News Detection via Heterogeneous Graph Neural Network
- Computer ScienceArXiv
- 2021
Hetero-SCAN, a novel social context aware fake news detection method, based on a heterogeneous graph neural network, is proposed, which yields significant improvement over state-of-the-art text-based and graphbasedfake news detection methods in terms of performance and efficiency.
Korean Fake News Detection with User Graph
- Physics
- 2021
사용자 그래프 기반 한국어 가짜뉴스 판별 방법 강명훈1,3◦, 서재형, 임희석2,3∗* 서울시립대학교 도시사회학과, 고려대학교 컴퓨터학과, Human-inspired AI 연구소 chaos038527@gmail.com {seojae777,limhseok}@korea.ac.kr Korean Fake News Detection with User Graph…
Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media
- Computer ScienceWWW
- 2022
A fake news detection model named Post-User Interaction Network (PSIN) is proposed, which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics.
References
SHOWING 1-10 OF 51 REFERENCES
CSI: A Hybrid Deep Model for Fake News Detection
- Computer ScienceCIKM
- 2017
This work proposes a model called CSI which is composed of three modules: Capture, Score, and Integrate, and incorporates the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.
Fake News Detection on Social Media using Geometric Deep Learning
- Computer ScienceArXiv
- 2019
A novel automatic fake news detection model based on geometric deep learning that can be reliably detected at an early stage, after just a few hours of propagation, and the results point to the promise of propagation-based approaches forfake news detection as an alternative or complementary strategy to content-based approach.
MVAE: Multimodal Variational Autoencoder for Fake News Detection
- Computer ScienceWWW
- 2019
An end-to-end network that uses a bimodal variational autoencoder coupled with a binary classifier for the task of fake news detection, which outperforms state-of-the-art methods by margins as large as ~ 6% in accuracy and ~ 5% in F1 scores.
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs
- Computer ScienceAAAI
- 2016
This paper discovers conflicting viewpoints in news tweets with a topic model method, and builds a credibility propagation network of tweets linked with supporting or opposing relations that generates the final evaluation result for news.
Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection
- Computer Science2019 IEEE International Conference on Data Mining (ICDM)
- 2019
A novel global-local attention network (GLAN) for rumor detection is presented, which jointly encodes the local semantic and global structural information in the message propagation graph.
Beyond News Contents : The Role of Social Context for Fake News Detection
- Computer Science
- 2018
A tri-relationship embedding framework TriFN is proposed, which models publisher-news relations and user-news interactions simultaneously forfake news classification and significantly outperforms other baseline methods for fake news detection.
News Credibility Evaluation on Microblog with a Hierarchical Propagation Model
- Computer Science2014 IEEE International Conference on Data Mining
- 2014
This work proposes a hierarchical propagation model for evaluating news credibility on Micro blog by formulating this propagation process as a graph optimization problem, and provides a globally optimal solution with an iterative algorithm.
Multiple Rumor Source Detection with Graph Convolutional Networks
- Computer ScienceCIKM
- 2019
This paper proposes a deep learning based model, namely GCNSI (Graph Convolutional Networks based Source Identification), to locate multiple rumor sources without prior knowledge of underlying propagation model by adopting spectral domain convolution.
Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks
- Computer ScienceAAAI
- 2018
The experimental results demonstrate that the proposed models can detect fake news with over 90% accuracy within five minutes after it starts to spread and before it is retweeted 50 times, which is significantly faster than state-of-the-art baselines.
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
- Computer ScienceArXiv
- 2018
To facilitate fake news related researches, a fake news data repository FakeNewsNet is provided, which contains two comprehensive datasets that includes news content, social context, and spatiotemporal information.