Sharing Behavior in Online Social Media: An Empirical Analysis with Deep Learning

  • Donghyuk Shin
  • Shu He
  • Gene Moo Lee
  • Andrew B. Whinston
Conference paper

DOI: 10.1007/978-3-319-45408-5_26

Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 258)
Cite this paper as:
Shin D., He S., Lee G.M., Whinston A.B. (2016) Sharing Behavior in Online Social Media: An Empirical Analysis with Deep Learning. In: Sugumaran V., Yoon V., Shaw M. (eds) E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life. WEB 2015. Lecture Notes in Business Information Processing, vol 258. Springer, Cham

Abstract

We conduct a large-scale empirical study on the sharing behavior in social media to measure the effect of message features and initial messengers on information diffusion. Our analysis focuses on messages created by companies and utilizes both textual and visual semantic content by employing state-of-the-art machine learning methods: topic modeling and deep learning. We find that messages with multiple conspicuous images and messengers with similar content are crucial in the diffusion process. Our approach for semantic content analysis, particularly for visual content, bridges advanced machine learning techniques for effective marketing and social media strategies.

Keywords

Social media Information diffusion Deep learning Topic modeling Community analysis 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Donghyuk Shin
    • 1
  • Shu He
    • 1
  • Gene Moo Lee
    • 2
  • Andrew B. Whinston
    • 1
  1. 1.University of Texas at AustinAustinUSA
  2. 2.University of Texas at ArlingtonArlingtonUSA

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