Predicting blogging behavior using temporal and social networks

Bi Chen; Qiankun Zhao; Bingjun Sun; Prasenjit Mitra

doi:10.1109/ICDM.2007.97

Predicting blogging behavior using temporal and social networks

Bi Chen, Qiankun Zhao, Bingjun Sun, Prasenjit Mitra

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

7 Scopus citations

Abstract

Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social-network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos, for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.

Original language	English (US)
Title of host publication	Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages	439-444
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2007.97
State	Published - 2007
Event	7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	7th IEEE International Conference on Data Mining, ICDM 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ICDM.2007.97

Cite this

@inproceedings{b76dc53538024be6acb1539baa79cdb2,

title = "Predicting blogging behavior using temporal and social networks",

abstract = "Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social-network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos, for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.",

author = "Bi Chen and Qiankun Zhao and Bingjun Sun and Prasenjit Mitra",

year = "2007",

doi = "10.1109/ICDM.2007.97",

language = "English (US)",

isbn = "0769530184",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "439--444",

booktitle = "Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007",

note = "7th IEEE International Conference on Data Mining, ICDM 2007 ; Conference date: 28-10-2007 Through 31-10-2007",

}

Chen, B, Zhao, Q, Sun, B & Mitra, P 2007, Predicting blogging behavior using temporal and social networks. in Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007., 4470270, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 439-444, 7th IEEE International Conference on Data Mining, ICDM 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDM.2007.97

Predicting blogging behavior using temporal and social networks. / Chen, Bi; Zhao, Qiankun; Sun, Bingjun et al.
Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. 2007. p. 439-444 4470270 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

TY - GEN

T1 - Predicting blogging behavior using temporal and social networks

AU - Chen, Bi

AU - Zhao, Qiankun

AU - Sun, Bingjun

AU - Mitra, Prasenjit

PY - 2007

Y1 - 2007

N2 - Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social-network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos, for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.

AB - Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social-network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos, for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.

UR - http://www.scopus.com/inward/record.url?scp=49849105113&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49849105113&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2007.97

DO - 10.1109/ICDM.2007.97

M3 - Conference contribution

AN - SCOPUS:49849105113

SN - 0769530184

SN - 9780769530185

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 439

EP - 444

BT - Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007

T2 - 7th IEEE International Conference on Data Mining, ICDM 2007

Y2 - 28 October 2007 through 31 October 2007

ER -

Predicting blogging behavior using temporal and social networks

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this