Date of Award

Fall 2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Information Technology & Decision Sciences

Program/Concentration

Business Administration-Information Technology

Committee Director

Ling Li

Committee Member

John Doukas

Committee Member

Harris Wu

Abstract

It is a life or death matter for a firm to observe its environment and identify new threats or opportunities quickly. Information technology has increased firm’s speed and agility in responding to environmental changes. Social media offers a vast and timely source of environmental information that firms can readily use gauge public sentiment. Twitter is a high-speed service that allows anyone to “tweet” a message to any interested parties. Firms can access near instantaneous changes in the public mood about any topic by using Sentiment Analysis. These topics range from predicting equities prices to predicting election outcomes. A gap exists in the literature because researchers discard tweets without any theoretically sound reason for doing so. We propose a framework that provides a theory-based justification for discarding data. We then explore the framework results using high frequency equity market prices. By examining the results of three case studies encompassing 57,600 OLS regressions and 1,887,408 tweets, our results indicate the framework yields higher quality results as measured by better R2 fits.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/swcq-ts12

ISBN

9780355778199

ORCID

0000-0003-3768-4357

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