Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks

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 tim...

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Autores principales:	Jing Wang, Hui Zhao
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Topic propagation user-role topic-influence probability model social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/dd31c7a692b0414f801b7d1e36701b0a
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spelling	oai:doaj.org-article:dd31c7a692b0414f801b7d1e36701b0a2021-11-25T00:00:29ZDynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks2169-353610.1109/ACCESS.2021.3126382https://doaj.org/article/dd31c7a692b0414f801b7d1e36701b0a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606746/https://doaj.org/toc/2169-3536Hot 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.Jing WangHui ZhaoIEEEarticleTopic propagationuser-roletopic-influenceprobability modelsocial networksElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154717-154730 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Topic propagation user-role topic-influence probability model social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Topic propagation user-role topic-influence probability model social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jing Wang Hui Zhao Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
description	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.
format	article
author	Jing Wang Hui Zhao
author_facet	Jing Wang Hui Zhao
author_sort	Jing Wang
title	Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
title_short	Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
title_full	Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
title_fullStr	Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
title_full_unstemmed	Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks
title_sort	dynamic analysis of user-role and topic-influence for topic propagation in social networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/dd31c7a692b0414f801b7d1e36701b0a
work_keys_str_mv	AT jingwang dynamicanalysisofuserroleandtopicinfluencefortopicpropagationinsocialnetworks AT huizhao dynamicanalysisofuserroleandtopicinfluencefortopicpropagationinsocialnetworks
_version_	1718414725936578560

Dynamic Analysis of User-Role and Topic-Influence for Topic Propagation in Social Networks

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