Diffusion of information in social networks

Is it all local?

Ceren Budak, Divyakant Agrawal, Amr El Abbadi

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

16 Citations (Scopus)

Abstract

Recent studies on the diffusion of information in social networks have largely focused on models based on the influence of local friends. In this paper, we challenge the generalizability of this approach and revive theories introduced by social scientists in the context of diffusion of innovations to model user behavior. To this end, we study various diffusion models in two different online social networks; Digg and Twitter. We first evaluate the applicability of two representative local influence models and show that the behavior of most social networks users are not captured by these local models. Next, driven by theories introduced in the diffusion of innovations research, we introduce a novel diffusion model called Gaussian Logit Curve Model (GLCM) that models user behavior with respect to the behavior of the general population. Our analysis shows that GLCM captures user behavior significantly better than local models, especially in the context of Digg. Aiming to capture both the local and global signals, we introduce various hybrid models and evaluate them through statistical methods. Our methodology models each user separately, automatically determining which users are driven by their local relations and which users are better defined through adopter categories, therefore capturing the complexity of human behavior.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages121-130
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: 10 Dec 201213 Dec 2012

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period10/12/1213/12/12

Fingerprint

Innovation
Statistical methods

Keywords

  • Diffusion models
  • Diffusion of innovations
  • Firth logistic regression
  • Gaussian logit curve
  • Social networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Budak, C., Agrawal, D., & El Abbadi, A. (2012). Diffusion of information in social networks: Is it all local? In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 121-130). [6413909] https://doi.org/10.1109/ICDM.2012.74

Diffusion of information in social networks : Is it all local? / Budak, Ceren; Agrawal, Divyakant; El Abbadi, Amr.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 121-130 6413909.

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

Budak, C, Agrawal, D & El Abbadi, A 2012, Diffusion of information in social networks: Is it all local? in Proceedings - IEEE International Conference on Data Mining, ICDM., 6413909, pp. 121-130, 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, 10/12/12. https://doi.org/10.1109/ICDM.2012.74
Budak C, Agrawal D, El Abbadi A. Diffusion of information in social networks: Is it all local? In Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 121-130. 6413909 https://doi.org/10.1109/ICDM.2012.74
Budak, Ceren ; Agrawal, Divyakant ; El Abbadi, Amr. / Diffusion of information in social networks : Is it all local?. Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. pp. 121-130
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