Predicting blogging behavior using temporal and social networks

Bi Chen, Qiankun Zhao, Bingjun Sun, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages439-444
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

Fingerprint

Blogs
Learning systems
Recommender systems
Marketing
Neural networks
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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 (pp. 439-444). [4470270] https://doi.org/10.1109/ICDM.2007.97
Chen, Bi ; Zhao, Qiankun ; Sun, Bingjun ; Mitra, Prasenjit. / Predicting blogging behavior using temporal and social networks. Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. 2007. pp. 439-444
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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.",
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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, 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; Mitra, Prasenjit.

Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. 2007. p. 439-444 4470270.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen B, Zhao Q, Sun B, Mitra P. Predicting blogging behavior using temporal and social networks. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. 2007. p. 439-444. 4470270 https://doi.org/10.1109/ICDM.2007.97