SimCLRT: A Simple Framework for Contrastive Learning of Rumor Tracking

As the second stage of the rumor defeat task pipeline, rumor tracking aims to filter tweets related to a specific event. However, due to the different levels of attention of different events, events related to fewer tweets may be masked by related to more tweets. In some research, the researchers give up detecting the events containing a small number of tweets for better results. To this end, we propose a Simple Framework for Contrastive Learning of Rumor Tracking (SimCLRT)-a novel rumor tracking framework that uses contrastive learning to alleviate the cover between tweets. SimCLRT contains three variants SimCLRT-CNN, SimCLRTLinear, and SimCLRT-RNN. We conduct experiments on the two commonly used rumor tracking datasets PHEME and RumorEval. The results show that SimCLRT completely defeated baselines. Like the detection performance on the events that contain many tweets, SimCLRT can also effectively detect events containing a small number of tweets. Furthermore, we compare and analyze the performance of SimCLRT variants. SimCLRT-CNN is the model that performs best in our experiments. Although SimCLRT-Linear has a slight advantage on the RumorEval dataset, its robustness is weaker than SimCLRT-RNN and SimCLRT-CNN. If in a long text environment, we consider SimCLRT-RNN will perform more competitively.

Automated summary

In this study, we propose a simple framework called SimCLRT for rumor tracking, which uses contrastive learning to alleviate the problem of tweet coverage. The experimental results show that SimCLRT has a good detection performance in different types of events, especially for events with a small number of tweets.