Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing rumor detection models focus on textual data to extract distinctive features, but they fail to capture the inconsistency information among the content and background knowledge. |
| Approach: | They propose to capture inconsistency semantics and content-knowledge level in a unified framework to detect rumors with multimedia content. |
| Outcome: | Extensive experiments on two public real-world datasets show that the proposed network outperforms the state-of-the-art models. |
Similar Papers
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content (D18-1)
Copied to clipboard
| Challenge: | rumors with multimedia content are becoming more and more common on social networks . a new feature set is proposed to verify rumors pivoting on multimedia content . |
| Approach: | They propose to use multimedia content to find external information on social media platforms . they propose to leverage semantic similarity between rumors and external information . |
| Outcome: | The proposed approach achieves state-of-the-art results on social networks . it leverages semantic similarity between rumors and external information . |
You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (2025.coling-main)
Copied to clipboard
| Challenge: | Existing rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Existing research mainly combats this with content and response-based detection methods. |
| Approach: | They propose a Large Language Model with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses. |
| Outcome: | The proposed approach can indicate the model’s uncertainty (i.e., reliability) of the results. |
Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features. |
| Approach: | They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies. |
| Outcome: | The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets . |
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies on rumor detection focus on text content and propagation structure . however, the uncertainty caused by unreliable relations in propagation structures is common . |
| Approach: | They propose a Bayesian-based model that captures propagation uncertainty for rumor detection. |
| Outcome: | The proposed model achieves better performance than baseline methods on rumor detection and early rumour detection tasks. |
Detection and Resolution of Rumors and Misinformation with NLP (2020.coling-tutorials)
Copied to clipboard
| Challenge: | Detecting false and misleading claims on the web is a sub-field of NLP . this half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
| Approach: | This half-day tutorial presents the theory behind false and misleading claims detection . it covers the steps involved in identifying check-worthy claims, tracking claims and rumors, rumor collection and annotation, grounding claims against knowledge bases, and using stance to verify claims. |
| Outcome: | This half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to defend against fake news are limited to text and metadata . authors identify weaknesses that adversaries can exploit by manipulating such technology . |
| Approach: | They propose a more realistic defense mechanism to defend against machine-generated news . they use a NeuralNews dataset to identify weaknesses that adversaries can exploit . |
| Outcome: | The proposed approach detects visual-semantic inconsistencies and provides a useful first line of defense against machine-generated disinformation. |
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. |
Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing detection models for rumors detection are poor interpretability and lack the textual content to detect rumors. |
| Approach: | They propose a framework that analyzes the textual content and propagation paths of rumors on social media and provides multi-perspective prediction explanations. |
| Outcome: | The proposed framework defends against malicious attacks and provides prediction explanations on three public datasets. |
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
Copied to clipboard
| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing rumor detection models neglect the semantic coherence between text and image components in multimodal posts . Existing models neglect incomplete modalities in single modal posts, such as missing text or images . |
| Approach: | They propose a framework for incomplete modality rumor detection that captures semantic consistency between text and image pairs while enhancing model generalization to incomplete modalities within individual posts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media. |