Learnable Models for Information Diffusion and its Associated User Behavior in Micro-blogosphere

Abstract

There are two major contributions: 1) a new efficient and effective method to detect a burst in information diffusion from the observed data, and 2) a new opinion formation model that takes people's past opinion into account. Both problems are formulated as a maximum likelihood problem in which the likelihood of observing the data from the model is maximized. For 1) the algorithm was tested against the real Twitter data of the 2011 Tohoku earthquake and tsunami, and for 2) the algorithm was tested against the real opinion diffusion data from a Japanese word-of-mouth communication site for cosmetics. Both confirmed that the algorithms are efficient and work as expected.

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Document Details

Document Type
Technical Report
Publication Date
Aug 30, 2012
Accession Number
ADA578681

Entities

People

  • Kazumi Saitō

Organizations

  • University of Shizuoka

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Change Detection
  • Computational Science
  • Data Mining
  • Information Processing
  • Information Science
  • Information Systems
  • Internet
  • Machine Learning
  • Network Science
  • Online Communications
  • Probabilistic Models
  • Probability Distributions
  • Social Media
  • Social Networking Services
  • Social Networks
  • Two Dimensional

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Educational Psychology
  • Operations Research