| dc.description.abstract | With the development of information technology and the Internet, the speed of information spreading has significantly increased, people on social media platforms usually are not able to effectively verify the source and credibility of the information. Unverified information spreading on the Internet was called online rumor. The rumor has become a severe problem, not only caused the social panic, but also changed the direction of public opinion. To increase people′s awareness of rumors, non-governmental organizations have established rumor query websites, such as Cofacts and Mygopen, which rely on manual verification methods on identifying online rumor. In academia, There are many researchers proposed deep learning and machine learning techniques for identifying rumor. However, if the architecture of deep learning model is too large, the process of training would be time-consuming. Although the machine learning model has an excellent accuracy, but it can not solve the sematic problem. In addition, if the user is unacceptable about the prediction results by the model, then a mechanism is needed to identify the online rumors by reasoning method and referring to similar cases.
Therefore, this research applies machine learning techniques and ontology models to predict online rumor and deal with antisense problem. Moreover, if the users do not accept the predicted results, then they could use case-based reasoning in a semi-automatic way to achieve online rumor identification. In conclusion, this research has implemented the proposed methodology into an online rumor identification system, and the users could access our system by website or Linebot. The system was verified by comparing the related machine model and the traditional Chinese-based system in practice. | en_US |