Abstract
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.
Highlights
With the rapid development of mobile Internet technology, online social networking (OSN), a novel information publishing and sharing platform, has become an essential part of our daily life
We evaluate that the knowledge related to large-scale datasets in the field of e-commerce reviews has similar features with the knowledge about the characteristics of rumors, which is used to train a model whose parameters is transferred to the rumor detection model
In terms of recall rate, Char-convolutional neural network (CNN) achieves the best results with a recall rate of 85.47%. e recall rate of our model is higher than the recall rates of both VDCNN and RCNN; it is lower than the recall rate of CharCNN. e reason for this is that our model considers the lower false positives as the most important guiding principle during the rumor detection process
Summary
With the rapid development of mobile Internet technology, online social networking (OSN), a novel information publishing and sharing platform, has become an essential part of our daily life. Rumors are the most common false information, which are false messages that spread among a large amount of people and have mislead these people [1]. Due to easy access to social media, rumors can spread extensively on social media, bringing huge harm to society and causing a lot of economic losses. Malicious rumors may seriously violate the opinions of OSN users, cause social panic, and even lead to a crisis of confidence. E rumors on OSN have become a serious social problem. Erefore, effective detection of rumors in OSN platforms is highly desired. E research studies on automatic detection of rumors have received increasing attention It is unrealistic to rely on manual methods to identify and filter rumors, and the average accuracy of three human judges is only 57.33% [2]. erefore, effective detection of rumors in OSN platforms is highly desired. e research studies on automatic detection of rumors have received increasing attention
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