An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors

Abstract EN

With the extensive usage of social media platforms, spam information, especially rumors, has become a serious problem of social network platforms.

The rumors make it difficult for people to get credible information from Internet and cause social panic.

Existing detection methods always rely on a large amount of training data.

However, the number of the identified rumors is always insufficient for developing a stable detection model.

To handle this problem, we proposed a deep transfer model to achieve accurate rumor detection in social media platforms.

In detail, an adaptive parameter tuning method is proposed to solve the negative transferring problem in the parameter transferring process.

Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.