Modeling and predicting the re-post behavior in Sina Weibo

Xinjiang Lu; Zhiwen Yu; Bin Guo; Xingshe Zhou

doi:10.1109/GreenCom-iThings-CPSCom.2013.166

Modeling and predicting the re-post behavior in Sina Weibo

Xinjiang Lu
, Zhiwen Yu
, Bin Guo
, Xingshe Zhou

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Study of human behavior patterns is of utmost importance to many areas, such as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, online social networks (OSNs) have become an attractive proxy for studying human behaviors. One of the interesting and challenging problems about OSNs is that how much attention of a post from a user can gain? In this paper, we try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a famous microblogging service in China. Specifically, we propose a Reposts Tree based method to model the reposting process in a temporal dynamic manner. Experiments over the real world collected data indicate that our method is effective on repost predicting.

Original language	English
Title of host publication	Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Pages	962-969
Number of pages	8
DOIs	https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.166
State	Published - 2013
Event	2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 - Beijing, China Duration: 20 Aug 2013 → 23 Aug 2013

Publication series

Name	Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

Conference

Conference	2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Country/Territory	China
City	Beijing
Period	20/08/13 → 23/08/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Reposts predicting
Sina weibo
Social media

Access to Document

10.1109/GreenCom-iThings-CPSCom.2013.166

Cite this

Lu, X., Yu, Z., Guo, B., & Zhou, X. (2013). Modeling and predicting the re-post behavior in Sina Weibo. In Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 (pp. 962-969). Article 6682180 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.166

Lu, Xinjiang ; Yu, Zhiwen ; Guo, Bin et al. / Modeling and predicting the re-post behavior in Sina Weibo. Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. pp. 962-969 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013).

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title = "Modeling and predicting the re-post behavior in Sina Weibo",

abstract = "Study of human behavior patterns is of utmost importance to many areas, such as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, online social networks (OSNs) have become an attractive proxy for studying human behaviors. One of the interesting and challenging problems about OSNs is that how much attention of a post from a user can gain? In this paper, we try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a famous microblogging service in China. Specifically, we propose a Reposts Tree based method to model the reposting process in a temporal dynamic manner. Experiments over the real world collected data indicate that our method is effective on repost predicting.",

keywords = "Reposts predicting, Sina weibo, Social media",

author = "Xinjiang Lu and Zhiwen Yu and Bin Guo and Xingshe Zhou",

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booktitle = "Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013",

note = "2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 ; Conference date: 20-08-2013 Through 23-08-2013",

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Lu, X, Yu, Z, Guo, B & Zhou, X 2013, Modeling and predicting the re-post behavior in Sina Weibo. in Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013., 6682180, Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, pp. 962-969, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, Beijing, China, 20/08/13. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.166

Modeling and predicting the re-post behavior in Sina Weibo. / Lu, Xinjiang; Yu, Zhiwen; Guo, Bin et al.
Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. p. 962-969 6682180 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modeling and predicting the re-post behavior in Sina Weibo

AU - Lu, Xinjiang

AU - Yu, Zhiwen

AU - Guo, Bin

AU - Zhou, Xingshe

PY - 2013

Y1 - 2013

N2 - Study of human behavior patterns is of utmost importance to many areas, such as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, online social networks (OSNs) have become an attractive proxy for studying human behaviors. One of the interesting and challenging problems about OSNs is that how much attention of a post from a user can gain? In this paper, we try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a famous microblogging service in China. Specifically, we propose a Reposts Tree based method to model the reposting process in a temporal dynamic manner. Experiments over the real world collected data indicate that our method is effective on repost predicting.

AB - Study of human behavior patterns is of utmost importance to many areas, such as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, online social networks (OSNs) have become an attractive proxy for studying human behaviors. One of the interesting and challenging problems about OSNs is that how much attention of a post from a user can gain? In this paper, we try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a famous microblogging service in China. Specifically, we propose a Reposts Tree based method to model the reposting process in a temporal dynamic manner. Experiments over the real world collected data indicate that our method is effective on repost predicting.

KW - Reposts predicting

KW - Sina weibo

KW - Social media

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DO - 10.1109/GreenCom-iThings-CPSCom.2013.166

M3 - 会议稿件

AN - SCOPUS:84893512168

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T3 - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

SP - 962

EP - 969

BT - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

T2 - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

Y2 - 20 August 2013 through 23 August 2013

ER -

Lu X, Yu Z, Guo B , Zhou X. Modeling and predicting the re-post behavior in Sina Weibo. In Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. p. 962-969. 6682180. (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013). doi: 10.1109/GreenCom-iThings-CPSCom.2013.166

Modeling and predicting the re-post behavior in Sina Weibo

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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