IDENTIFYING RELIABLE POSTS AND USERS IN ONLINE SOCIAL NETWORKS

In the age of Web 2.0, generalizing concepts are mainly based on information sharing and user cooperation. The user-centric Internet mode encourages more users to participate in the Internet. However, this mode causes the flooding of social networks with low-quality information. Thus, identifying trusted information in online social networks (OSNs) so as to enable the Internet to serve humans better has gradually become a research hotspot. However, most studies evaluate the quality of user-created content or discriminate reliable users in isolation. These results are specific to particular social networks and lack generality. Intuitively, reliable content and users are closely related. In this study, the authors attempt to combine these two types of reliable data by separately identifying reliable posts and users in a social network. Posts and users are found to improve each other based on the preliminary identification results. To deal with imbalanced data, an algorithm that combines oversampling and undersampling is used to build balanced data. An ensemble classifier is adopted for data classification. Experiments show that the proposed framework is both effective and efficient for most types of OSNs. The contributions of this study are two-fold: (i) a framework combining user-created content and reliable user recognition is proposed, and (ii) an ensemble classifier is built for use in data classification.