Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to learn from social-media conversations have been proposed to identify and contain fake news shared on social media platforms. |
| Approach: | They propose to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. |
| Outcome: | The proposed models outperform the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively. |
Similar Papers
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity (D19-1)
Copied to clipboard
| Challenge: | Existing methods to verify rumors are needed to identify false rumors. |
| Approach: | They propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter that exploits the temporal dynamics of stance evolution. |
| Outcome: | The proposed framework outperforms previous methods on two benchmark datasets showing that it can predict rumor stance and veracity. |
Debunking Rumors on Twitter with Tree Transformer (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for rumor detection follow tree edges or treat all posts fully-connected during feature learning. |
| Approach: | They propose a new rumor detection model based on tree transformer to better utilize user interactions in the dialogue . they propose to use post-level self-attention to aggregate the intra-/inter-subtree stances . |
| Outcome: | The proposed model improves rumor detection performance on social media conversations . it is based on a conversation tree that encodes important information indicative of credibility . |
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. |
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 on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing models to classify rumors have low precision and are time consuming. |
| Approach: | They propose a multiloss hierarchical biLSTM model with an attenuation factor that can extract deep information from limited quantities of text. |
| Outcome: | The proposed model can extract deep information from limited quantities of text. |
Tree Communication Models for Sentiment Analysis (P19-1)
Copied to clipboard
| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
| Approach: | They propose a tree-based sentiment analysis model using graph convolutional neural network and graph recurrent neural network which allows rich information exchange between phrases constituent tree. |
| Outcome: | The proposed model outperforms existing tree-LSTMs in accuracy and efficiency, providing more consistent predictions on phrase-level sentiments. |
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies . |
| Approach: | They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV. |
| Outcome: | The proposed model outperforms existing methods on two benchmark datasets. |
Can Rumour Stance Alone Predict Veracity? (C18-1)
Copied to clipboard
| Challenge: | Existing studies of automatic veracity classification of social media rumours have not explored the effectiveness of crowd stance to determine veracity. |
| Approach: | They propose to use stance as an additional feature to those commonly used in earlier studies to model the veracity of a rumour using Hidden Markov Models and collective stance information to model a social media rumor. |
| Outcome: | The proposed models outperform those using crowd stance and tweets’ times as the only features for modelling true and false rumours. |
RP-DNN: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for early rumor detection on social media platforms are limited, incomplete and noisy. |
| Approach: | They propose a novel hybrid neural network architecture which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets. |
| Outcome: | The proposed model achieves state-of-the-art for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors. |
Exploiting Microblog Conversation Structures to Detect Rumors (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for rumor detection ignore the conversation structure of tweets . 68% of american adults occasionally read news on social media platforms . however, the credibility of news propagated through social media is questionable due to the lack of editors who can validate it. |
| Approach: | They propose to model Twitter conversation structure by modeling it as a graph to detect rumors by reading tweets that voice other users’ stances on the tweet. |
| Outcome: | The proposed model outperforms baseline models on two rumor datasets and shows that it outperformed several baseline models. |