Find in Library

In social networks, a large amount of spam has seriously threaten users' information security and the credit system of social websites. Aiming at the noise and sparsity problems, an attention-based CNN method was proposed to detect spam. On the basis of classical CNN, this method added a filter layer in which an attention mechanism based on Naive Bayesian weighting technology was designed to solve the noise issue. What’s more, instead of the original pooling strategy, it adapted an attention-based pooling policy to alleviate the sparsity problem. Compared with other methods, the results show that the accuracy has increased by 1.32%，2.15%，0.07%，1.63% on four different data sets.