Open AccessAn Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors
Author(s) -
Meicheng Guo,
Zhiwei Xu,
Limin Liu,
Mengjie Guo,
Yujun Zhang
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/7562567
Subject(s) - rumor , computer science , social media , process (computing) , transfer of learning , the internet , microblogging , machine learning , artificial intelligence , social network (sociolinguistics) , data mining , computer security , world wide web , public relations , political science , operating system
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.