LAUSR

The amount of training data in the field of Weibo rumor detection is small, and online news changes constantly, but the existing rumor detection models do not have the ability of continuous learning, they also cannot achieve knowledge accumulation and update, they usually need a large number of training examples to improve the effect. In contrast, Lifelong Machine Learning (LML) paradigm has the ability of continuous learning, which retains the knowledge learned in the past and uses it to help future learning tasks, after learning, some knowledge is updated. With the growth and update of knowledge, the performance of each task will be better and better. Hence, we use this paradigm to build a Weibo rumor detection model. We firstly extracted three types of features based on content, user, and propagation from Weibo events, and proposed three new propagation features and Bidirectional Encoder Representations from Transformers (BERT) semantic features of source message for rumor detection. We then used Simulated Annealing (SA) to improve the Genetic Algorithm (GA), which was called GA-SA used to search for the best global minimum feature subset to improve the classification effect of the Efficient Lifelong Learning Algorithm (ELLA) on rumors. In the continuous learning process, the ELLA transfers knowledge to learn new tasks and refines knowledge over time to maximize performance across all tasks. The proposed model is called GA-SA-ELLA. The experimental results show that our model even in the case of less training data for each task could achieve superior detection results.