Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

Abstract EN

Purpose.

The purpose of this article is to predict the topic popularity on the social network accurately.

Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP).

Design/Methodology/Approach.

Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up.

Findings.

The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%.

The training speed is higher and the prediction precision is higher.

Practical Implications.

The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities.

Moreover, its future popularity can be predicted by deep learning methods.

At the same time, a new application field of deep learning technology has been further discovered and verified.

Originality/Value.

This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.