Fad or here to stay: Predicting product market adoption and longevity using large scale, social media data

Suppawong Tuarob, Conrad S. Tucker

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

45 Scopus citations

Abstract

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter R ® and Facebook R ® , have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users' opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

Original languageEnglish (US)
Title of host publication33rd Computers and Information in Engineering Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791855867
DOIs
StatePublished - Jan 1 2013
EventASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2013 - Portland, OR, United States
Duration: Aug 4 2013Aug 7 2013

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2 B

Other

OtherASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2013
CountryUnited States
CityPortland, OR
Period8/4/138/7/13

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Tuarob, S., & Tucker, C. S. (2013). Fad or here to stay: Predicting product market adoption and longevity using large scale, social media data. In 33rd Computers and Information in Engineering Conference [V02BT02A012] (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2 B). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2013-12661