In Event-Based Social Networks (EBSNs), generating the event description of social events automatically is helpful for the organizer, so as to avoid the problems of poor description, descripting too much and low accuracy, and be easy to form rich, accurate and attractive event description. In order to automatically generate text that is sufficiently similar to true event description, a Generative Adversarial Network (GAN) model named GAN_PG was proposed to generate event description. In the GAN_PG model, the Variational Auto-Encoder (VAE) was used as the generator, and the neural network with the Gated Recurrent Unit (GRU) was used as the discriminator. In the model training, the Policy Gradient (PG) decline in reinforcement learning was used as reference, and a reasonable reward function was designed to train the generator to generate event description. Experimental results showed that the BLEU-4 value of the event description generated by GAN_PG reached 0.67, which proved that the event description generation model GAN_PG can generate event descriptions sufficiently similar to natural language in an unsupervised way.

In the related research of Event Based Social Networks (EBSNs), it is difficult to predict the participation of social events based on event description. The related studies are very limited, and the research difficulty mainly comes from the evaluation subjectivity of event description and limitations of language modeling algorithms. To solve these problems, first the concepts of successful event, similar event and event similarity were defined. Based on these concepts, the social data collected from the Meetup platform was extracted. At the same time, the analysis and prediction methods based on Lasso regression, Convolutional Neural Network (CNN) and Gated Recurrent Neural Network (GRNN) were separately designed. In the experiment, part of the extracted data was selected to train the three models, and the remaining data was used for the analysis and prediction. The results showed that, compared with the events without event description, the prediction accuracy of the events processed by the Lasso regression model was improved by 2.35% to 3.8% in different classifiers, and the prediction accuracy of the events processed by the GRNN model was improved by 4.5% to 8.9%, and the result of the CNN model processing was not ideal. This study proves that event description can improve event participation, and the GRNN model has the highest prediction accuracy among the three models.