| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Similar Papers
Examining Temporalities on Stance Detection towards COVID-19 Vaccination (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. |
| Approach: | They evaluate a range of transformer-based models using chronological and random splits of social media data to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination. |
| Outcome: | The proposed models show that the models performed better with chronological and random splits than with random split models. |
A Few Topical Tweets are Enough for Effective User Stance Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent work that employs unsupervised classification has shown that user stance detection is highly accurate on vocal Twitter users, but fails for less vocal users, who may have only authored a few tweets about a target. |
| Approach: | They propose to perform user stance detection on vocal Twitter users using contextualized embeddings to represent tweets and then perform unsupervised classification of the user. |
| Outcome: | The proposed method outperforms two baseline methods and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. |
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
Copied to clipboard
| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
Adversarial Learning for Zero-Shot Stance Detection on Social Media (2021.naacl-main)
Copied to clipboard
| Challenge: | a new model for zero-shot stance detection on Twitter uses adversarial learning to generalize across topics . previous work on zero- shot stance detector on English social media focuses on cross-target stances . |
| Approach: | They propose a model that uses adversarial learning to generalize across topics on Twitter . their model achieves state-of-the-art performance on unseen test topics . |
| Outcome: | The proposed model achieves state-of-the-art performance on unseen topics with minimal computational costs. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
Copied to clipboard
Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing stance detection datasets are complex deep neural networks, making them difficult to interpret. |
| Approach: | They propose a new large dataset free of such biases and demonstrate its aptness on existing stance detection systems. |
| Outcome: | The proposed model achieves human-level performance on the WT–WT dataset and more than two-third accuracy on other datasets. |
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have focused on topic-specific stance classifiers that fail to generalize to unseen topics. |
| Approach: | They propose to use contrastive learning and an unlabeled dataset to train topic-agnostic/TAG and topic-aligned/TAW embeddings for use in downstream stance detection. |
| Outcome: | The proposed model achieves state-of-the-art performance across several public stance detection datasets (0.771 F1-score on the Zero-shot VAST dataset). |
Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
| Approach: | They propose a multitasking model that extracts valence, arousal, and dominance aspects hidden in tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance. |
| Outcome: | The proposed model exploits the toxicity, morality, and speech act features of the tweets to detect the public's stance. |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
| Approach: | They propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. |
| Outcome: | The proposed framework shows that it can be used to predict unseen labels over strong baselines. |