The rapid development of social networks has a wide range of social effects,
which facilitates the study of social issues. Accurately forecasting the
information propagation process within social networks is crucial for promptly
understanding the event direction and effectively addressing social problems in
a scientific manner. The relationships between non-adjacent users and the
attitudes of users significantly influence the information propagation process
within social networks. However, existing research has ignored these two
elements, which poses challenges for accurately predicting the information
propagation process. This limitation significantly hinders the study of
emotional contagion and influence maximization in social networks. To address
these issues, by considering the relationships between non-adjacent users and
the influence of user attitudes, we propose a new information propagation model
based on the independent cascade model. Experimental results obtained from six
real Weibo datasets validate the effectiveness of the proposed model, which is
reflected in increased prediction accuracy and reduced time complexity.
Furthermore, the information dissemination trend in social networks predicted
by the proposed model closely resembles the actual information propagation
process, which demonstrates the superiority of the proposed model