机构:
Shandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
CSIRO, Waite Campus, Urrbrae, SA 5064, AustraliaShandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
Gao, Yichang
[1
,2
]
Sun, Yingping
论文数: 0引用数: 0
h-index: 0
机构:
Shandong Normal Univ, Business Sch, Jinan 250014, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
Sun, Yingping
[1
]
Zhang, Lidi
论文数: 0引用数: 0
h-index: 0
机构:
Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
Zhang, Lidi
[3
]
Liu, Fengming
论文数: 0引用数: 0
h-index: 0
机构:
Shandong Normal Univ, Business Sch, Jinan 250014, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
Liu, Fengming
[1
]
Gao, Lei
论文数: 0引用数: 0
h-index: 0
机构:
CSIRO, Waite Campus, Urrbrae, SA 5064, AustraliaShandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
Gao, Lei
[2
]
机构:
[1] Shandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
[2] CSIRO, Waite Campus, Urrbrae, SA 5064, Australia
[3] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China
Key rumor refuters;
Echo chamber;
Social media;
Complex network;
Weibo;
User behaviors;
DENYING RUMORS;
MISINFORMATION;
COMMUNICATION;
TRANSMISSION;
COMMUNITY;
DYNAMICS;
MODEL;
D O I:
10.1016/j.eswa.2023.120603
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
As one of the most intuitive and efficient rumor combating approaches, rumor rebuttal has been widely studied by the scientific community. However, existing studies on rumor refutation often ignore the relationship between communication network structures and user interaction behaviors. During the dissemination of rumor refutation information, some users of social media play a key role in influencing the behaviors of other users and leading the direction of public opinion. Identifying these key rumor refuters is therefore critical to the spread of rumor refutation information. Therefore, based on Sina Weibo rumor refuter data and combined with the echo chamber effect, we construct a multilayer network from three dimensions of users, events, and echo chambers, and develop a key rumor refuter identification model. Then, ten key indicators for user interaction behaviors are extracted based on group theory, and the identified key rumor refuters are comprehensively evaluated based on text and emotion analyses. We find that the positive echo chamber members are significantly more involved in the dissemination of rumor refutation information than negative members. Finally, according to the findings of this study, we suggest using differentiated management measures to effectively spread rumor refutation information by inserting positive users and isolating negative users with different emotions in the dissemination routes of rumor refutation information. This study offers a new method for rumor control and rumor refutation information dissemination.