Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to detect fake news focus on mining lexical and syntactic features. |
| Approach: | They propose a topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks to identify fake news on social media. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world datasets. |
Similar Papers
Entity-Aware Dual Co-Attention Network for Fake News Detection (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing models for fake news detection are limited in their ability to detect it from different aspects. |
| Approach: | They propose a Dual Co-Attention Network (Dual-CAN) for fake news detection that takes news content, social media replies, and external knowledge into consideration. |
| Outcome: | The proposed model outperforms existing models in two benchmark datasets. |
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to fact-check information focus on word-level attention or evidence-level focus, which may result in suboptimal performance. |
| Approach: | They propose a Hierarchical Multi-head Attentive Network to fact-check textual claims using word-level attention and document-level focus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-word datasets. Improvements over baselines are from 6% to 18%. |
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
Copied to clipboard
| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
Learning Hierarchical Discourse-level Structure for Fake News Detection (N19-1)
Copied to clipboard
| Challenge: | Existing methods for capturing discourse-level structure of fake news articles rely on annotated corpora. |
| Approach: | They propose to incorporate hierarchical discourse-level structure of fake and real news articles into detection methods . they propose to learn and construct a discourse- level structure for fake/real news articles . |
| Outcome: | The proposed approach can detect fake news articles based on their contents . it can also identify structure-related properties that can boost fake news understating . |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to detect fake news on social media are based on textual features and advanced linguistic features. |
| Approach: | They propose a neural network-based model to detect fake news on social media . they use a short-text tweet and a sequence of retweets without text comments to predict whether the source tweet is fake or not. |
| Outcome: | The proposed model outperforms state-of-the-art methods by 16% on real tweet datasets and produces reasonable explanations. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
Graph-based Fake News Detection using a Summarization Technique (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to detect fake news using external information and internal information are difficult to identify external information in all documents. |
| Approach: | They propose a graph-based fake news detection method that uses only the document internal information to represent the relationship between all sentences using a diagram and the reflection rate of contextual information among sentences is computed by using an attention mechanism. |
| Outcome: | The proposed method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method. |
KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities. |
| Approach: | They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario. |
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge. |
| Approach: | They propose a graph neural model which compares news to knowledge base through entities for fake news detection. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets. |