A Computational Social Science Framework for Learning and Visualizing the Latent Language of Structured IoT Interaction Data

61 Pages Posted: 7 Nov 2018

See all articles by Thomas Novak

Thomas Novak

George Washington University School of Business

Donna L. Hoffman

George Washington University School of Business

Date Written: October 31, 2018

Abstract

The Internet of Things (IoT), comprised of billions of smart devices representing trillions of interactions, has the potential to generate entirely new consumer experiences. In this paper, we develop a computational social science framework, grounded in assemblage theory concepts, to extract the shape and structure of consumer experience from the language of IoT interactions rendered as structured text. Our multi-stage framework uniquely integrates methods from computational linguistics (word2vec), unsupervised machine learning (t-SNE), and computational topology (topological data analysis) to: 1) identify and visualize the structure of the segments of consumer experience based on the similarity between IoT interaction events, and 2) for any given IoT interaction event, discover similar events that can further exploit current use and help explore new uses. Because the results are extracted from the actual interactions consumers engage in when they connect devices and services together, in the language in which they connect them, our framework can help consumers expand their use of the IoT and help marketers better target their marketing and communications programs and product and business development efforts.

Keywords: Assemblage Theory, Discovery, Internet of Things, Topological Data Analysis, Word2vec, Exploration, Exploitation, TDA, t-SNE, Visualization

Suggested Citation

Novak, Thomas and Hoffman, Donna L., A Computational Social Science Framework for Learning and Visualizing the Latent Language of Structured IoT Interaction Data (October 31, 2018). Available at SSRN: https://ssrn.com/abstract=3278045 or http://dx.doi.org/10.2139/ssrn.3278045

Thomas Novak (Contact Author)

George Washington University School of Business ( email )

2121 I Street NW
Washington, DC 20052
United States
9515431592 (Phone)

Donna L. Hoffman

George Washington University School of Business ( email )

2201
G St NW
Washington, DC 20052
United States
9515431260 (Phone)

HOME PAGE: http://postsocial.gwu.edu

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