A metric-learning method for few-shot cross-event rumor detection

Abstract

With the rapid development of social media, quickly detecting rumors on social media has become vitally crucial. However, there exists the following challenges. 1) Rumors are always flooded with time-critical events, where large-scale labeled datasets are difficult to obtain. 2) Although historic events have sufficient labels, the performance of models tends to degrade on the newly emergent events due to event difference shift. Facing these challenges, in this study, we attempt to leverage the idea of few-shot learning which aims to quickly acquire knowledge on unseen events with a few labeled samples by sufficiently learning from old events with a number of verified samples. Different from few-shot learning tasks in the literature, rumors in the same class of different events (no matter historical events or new events) are likely to contain coincident features in their embedding space. Therefore, we validate two classical metric learning methods, the prototypical network (PN) and the relation network (RN) which are able to capture the class-level representations in few-shot learning settings, to explore the effectiveness of metric learning methods for cross-event rumor detection. Our proposed model contains two stages corresponding to Base-Classifier pre-trained module and Base-Meta training module. The Base-Classifier pre-trained module is a classification model trained on old events. Then its last layer for class prediction is removed and the remains of the module is viewed as an encoder. The Base-Meta training module fine-tunes the encoder, meanwhile trains a selected metric learning model with episodic training strategies on old events. Empirical tests on novel events have showed that our model can outperform the state-of-the-art baseline models on the benchmark cross-event rumor datasets PHEME5 and PHEME9. What’s more, the model can improve the performance of cross-data rumor detection, where the model is trained on Twitter15, but tested on Twitter16.