Sharing behavior in online social media: An empirical analysis with deep learning

Donghyuk Shin, Shu He, Gene Moo Lee, Andrew B. Whinston

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

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.

Original languageEnglish (US)
Title of host publicationE-Life
Subtitle of host publicationWeb-Enabled Convergence of Commerce, Work, and Social Life - 15th Workshop on e-Business, WEB 2015, Revised Selected Papers
EditorsVijayan Sugumaran, Victoria Yoon, Michael J. Shaw
PublisherSpringer Verlag
Pages222-227
Number of pages6
ISBN (Print)9783319454078
DOIs
StatePublished - 2016
Externally publishedYes
Event15th Workshop on e-Business on E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, WEB 2015 - Fort Worth, United States
Duration: Dec 12 2015Dec 12 2015

Publication series

NameLecture Notes in Business Information Processing
Volume258
ISSN (Print)1865-1348

Conference

Conference15th Workshop on e-Business on E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, WEB 2015
CountryUnited States
CityFort Worth
Period12/12/1512/12/15

Keywords

  • Community analysis
  • Deep learning
  • Information diffusion
  • Social media
  • Topic modeling

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Business and International Management
  • Information Systems
  • Modeling and Simulation
  • Information Systems and Management

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