近年来, 移动信息技术的迅速发展使得在线社交网络进入 高速发展阶段, 用户可在社交网络中进行信息交互, 但网络中具有强影 响力的不可信用户往往会引导大众产生情感偏差和信息偏误, 这严重影 响了网络安全, 故研究社交网络中的用户可信度量模型迫在眉睫. 结合社 交网络用户节点的网络链接结构、情感和热度, 以识别和评估社交网络中的信息来 源及用户可信度, 并结合真实社交网络数据将其与4种机器学习方法进行比较 实验, 以确保模型科学性. 实验结果显示: 所提PSB用户可信度量方法在准确度、 特异性和敏感性等方面都优于其他方法; 可信用户往往具有相对正面的情感, 且其比不 可信用户发布的信息更长、创建账户的时间更长、更容易被其他用户关注和提及; 而不可信用户发布的信息往往包含更多的标签和网址.

In recent years, the rapid development of mobile information technology has made online social network service enter the stage of rapid development. Users could exchange information on social networks. But those unreliable users who have strong influences on the network tend to make the public generate emotional deviation and information biases, which seriously affects the internet safety. Therefore, it is extremely urgent to research the user trust degree measurement model on social networks. Integrated with the network link structure, emotion and heat topics of the user nodes on social networks to identify and evaluate the information resources and user trust degree on it. Also, combined the real social network data to do a comparison experiment between the UTD and four kinds of machine learning methods, so as to confirm the scientific nature of the model. The experiential results showed that the PSB user trust degree measurement method we mentioned is better than other methods on the aspects of accuracy, specificity and sensitivity. The reliable users tend to have positive emotions and the information they post, the time they created the accounts are longer as well as they are easier to be followed and mentioned by other users comparing with unreliable users. However, the information the unreliable users post tends to be contained more labels and website addresses.