Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload
Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion un...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/914907 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554226172035072 |
|---|---|
| author | Pei Li Wei Li Hui Wang Xin Zhang |
| author_facet | Pei Li Wei Li Hui Wang Xin Zhang |
| author_sort | Pei Li |
| collection | DOAJ |
| description | Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations. |
| format | Article |
| id | doaj-art-7ccfa57a1b294244b0b29955bfc8d2ba |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7ccfa57a1b294244b0b29955bfc8d2ba2025-02-03T05:52:08ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/914907914907Modeling of Information Diffusion in Twitter-Like Social Networks under Information OverloadPei Li0Wei Li1Hui Wang2Xin Zhang3College of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaDue to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.http://dx.doi.org/10.1155/2014/914907 |
| spellingShingle | Pei Li Wei Li Hui Wang Xin Zhang Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload The Scientific World Journal |
| title | Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload |
| title_full | Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload |
| title_fullStr | Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload |
| title_full_unstemmed | Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload |
| title_short | Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload |
| title_sort | modeling of information diffusion in twitter like social networks under information overload |
| url | http://dx.doi.org/10.1155/2014/914907 |
| work_keys_str_mv | AT peili modelingofinformationdiffusionintwitterlikesocialnetworksunderinformationoverload AT weili modelingofinformationdiffusionintwitterlikesocialnetworksunderinformationoverload AT huiwang modelingofinformationdiffusionintwitterlikesocialnetworksunderinformationoverload AT xinzhang modelingofinformationdiffusionintwitterlikesocialnetworksunderinformationoverload |