Open AccessDynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
Author(s) -
Jing Wang,
Hui Zhao
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3126382
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Hot events spread quickly on social networks. Predicting event diffusion on social networks, also known as topic propagation, is an important task. The two important factors that affect topic propagation are users and topics, and both users’ roles and topics’ influences are time dependent on social networks. However, existing studies have largely overlooked this fact, so topic propagation prediction is still a major challenge. In this paper, a Topic Propagation Prediction method is proposed based on Dynamic Analysis of user-role and topic-influence, named TPP-DA, which predicts the topic propagation on social networks from both users’ and topics’ perspectives. First, we introduce a temporal perspective to improve the static analysis to the dynamic analysis of user-role, which is more adaptable to the changeable user-roles on social networks. Second, we introduce a metric called the topic heat to dynamically analyze the topic-influence on a single user and social group. Third, we combine the dynamic analysis of user-role and topic-influence with a weighted probability model to accurately predict topic propagation trends. The weights are determined by the dynamic analysis of user-role and topic-influence. Finally, several experiments are conducted to evaluate TPP-DA. Compared with TPP, the average error rate of TPP-DA is reduced by approximately 33%, which proves the efficiency of TPP-DA.