Using Deep Learning to Predict User Behavior in the Online Discussion

Communications in Computer and Information Science. 2019.

Karpov N., Demidovskij A.

Abstract. Popularity of social networks makes them an attractive field for analysis of users behavior, for example, based on the intention analysis of their posts and comments. In the linguistic theory only 25 types of intentions exist and can be joined in 5 supergroups. We use the dataset that contains directed oriented graphs which nodes store information about the author intention, text of the post in the social network Vkontakte etc. Each graph is split in a linked list of nodes (a sequence, 13156 sequences in our dataset) from root to each leaf so that the intention
prediction becomes the sequence prediction. We have analyzed traditional and neuronet approaches that address this task and proposed to solve it with the original modifications of CNN and RNN architectures. It was decided to translate all posts to the embeddings which are then used as inputs for our neural network. According to the benchmarking experiments, we have identified that the proposed RNN architecture outperforms other alternatives. Also, predicting supergroups is done more accurately. Finally, we found out, that the context in the dialogs is lost
quickly that allows to decrease the algorithm context size while keeping accuracy at the appropriate level.
 

Keywords: machine learning, artificial neural networks, social network analysis, sequence prediction

Publication based on the results of: Разработка и апробация эффективных методов классификации для больших баз мультимедийных данных (2017)