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
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages439-444
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE
Duration: 28 Oct 200731 Oct 2007

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
CityOmaha, NE
Period28/10/0731/10/07

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chen, B., Zhao, Q., Sun, B., & Mitra, P. (2007). Predicting blogging behavior using temporal and social networks. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 439-444). [4470270] https://doi.org/10.1109/ICDM.2007.97