Papers: User needs and preferences elicitation

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

[+] Author and Article Information
Suppawong Tuarob

Computer Science and Engineering,
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: suppawong@psu.edu

Conrad S. Tucker

Engineering Design and Industrial
and Manufacturing Engineering,
Computer Science and Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu

Though one could infer the relationship among Twitter users by constructing communities based on the Reply-To connections, such connections are sparse and spurious. These are not taken into account in most network-based leader identification algorithms.

A product domain is a set of products that belong to the same category, e.g., smartphone, automobile, laptop, etc.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 15, 2014; final manuscript received February 9, 2015; published online May 19, 2015. Assoc. Editor: Wei Chen.

J. Mech. Des 137(7), 071402 (Jul 01, 2015) (11 pages) Paper No: MD-14-1611; doi: 10.1115/1.4030049 History: Received September 15, 2014; Revised February 09, 2015; Online May 19, 2015

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

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Grahic Jump Location
Fig. 1

Overview of the proposed methodology

Grahic Jump Location
Fig. 2

Overview of the proposed methodology

Grahic Jump Location
Fig. 3

Monthly distribution of Twitter discussion of each smartphone model across the 31 month period of data collection

Grahic Jump Location
Fig. 4

Histogram showing the distribution of the FF-IPF scores of 25,816 total extracted global latent features

Grahic Jump Location
Fig. 5

Proportion of smartphone tweets which discuss the waterproof feature



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