A sword with two edges: propagation studies on both positive and negative information in online social networks

Wen, Sheng, Haghighi, Mohammad Sayad, Chen, Chao, Xiang, Yang, Zhou, Wanlei and Jia, Weijia 2015, A sword with two edges: propagation studies on both positive and negative information in online social networks, IEEE transactions on computers, vol. 64, no. 3, pp. 640-653, doi: 10.1109/TC.2013.2295802.

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Title A sword with two edges: propagation studies on both positive and negative information in online social networks
Author(s) Wen, Sheng
Haghighi, Mohammad Sayad
Chen, Chao
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Jia, Weijia
Journal name IEEE transactions on computers
Volume number 64
Issue number 3
Start page 640
End page 653
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-03
ISSN 0018-9340
Keyword(s) Science & Technology
Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Engineering
Social network
modeling
propagation analysis
COMPLEX NETWORKS
DISSEMINATION
WORMS
THRESHOLD
DYNAMICS
SENSOR
Summary Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people's behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.
Language eng
DOI 10.1109/TC.2013.2295802
Field of Research 080501 Distributed and Grid Systems
0803 Computer Software
0805 Distributed Computing
1006 Computer Hardware
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077754

Document type: Journal Article
Collections: School of Information Technology
2018 ERA Submission
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