Prediction of Message Diffusion: A Deep Learning Approach on Social Networks

Subject Areas : International Journal of Finance, Accounting and Economics Studies

husnyeh safearyan ¹ , Mohammad Jafar Tarokh ² , Mohammad Ali Afshar Kazemi ³

1 - P.hd Student, Faculty of Management and Economic, Science & Research Branch, Islamic Azad University, Tehran, Iran
2 - Professor, IT Group ,Faculty of Industrial Engineering, K. N. Toosi University of Technology Tehran, Iran.
3 - Associate Professor, Management Group, Faculty of Management ,Tehran North Branch, Islamic Azad University, Tehran, Iran

Received: 2022-01-01 Accepted : 2022-03-29 Published : 2022-12-01

Keywords: Social network, users, deep learning, Prediction, influential content,

Abstract :

Nowadays, many industries pay attention to social media because people are spending sizable chunks of their lives in virtual worlds. Some of the social networks such as Facebook, Instagram and Twitter affected by their user through content. Predicting the popularity of content can play an important role in different areas such as viral marketing, advertising and propagation news. However, prediction problem is a challenging problem. In this paper, we developed a deep learning approach to predict the popularity of tweets in the twitter social network. It is called DLMD. We extracted the feature of content from each tweet. We use the deep learning approach for prediction it means that we model this problem with a binary classification problem Our proposed method evaluate with different measures and the results show that DLMD method has a high accuracy in prediction rather than other methods. Therefore, DLMD is a convenient method to predict diffusion on the social networks.