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Citations since 2017
Publications
Publications (7)
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. Most of the existing methods have largely worked on event-level detection that requires the collection of posts relevant to a specific event and relied only on user-generated content. They are not appropriate to dete...
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. Most of the existing methods have largely worked on event-level detection that requires the collection of posts relevant to a specific event and relied only on user-generated content. They are not appropriate to dete...
The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment...
The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment...
We address the problem of making sense of rumour evolution during crises and emergencies. We study how understanding and capturing emerging rumours can benefit decision makers during such event. To this end, we propose a two-step framework for detecting rumours during crises. In the first step, we introduce an algorithm to identify noteworthy sub-e...
In this paper, we address the challenge of limited labeled data and class imbalance problem for machine-learning-based rumor detection in social media. We present an offline data augmentation method based on semantic relatedness for rumor detection. Unlabeled social media data is exploited to augment limited labeled data. Context-aware neural langu...
Projects
Project (1)
This is a research project that work towards early rumour detection. Our Rumour detection task is to identify pieces of information on social media that is need to be verified. Manual annotation of large-scale and noisy social media data for rumors is highly labor-intensive, time-consuming and requires special skills and insight to a specific event. Existing rumor dataset are in relatively small size and suffer high class imbalance. We are focusing on three problems: 1) the challenge of data scarcity; 2) modeling algorithm(temporal, propagation) with limited information for early rumor detection at message-level; 3) standardising evaluation and dataset.
