title: Mining Twitter to Assess the Public Perception of the "Internet of Things".
abstract: Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public's attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.
authors.affiliation: Health Outcomes & Policy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Jiang
authors.identifier: http://orcid.org/0000-0002-2238-5429
authors.initials: J
authors.lastname: Bian
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock, Little Rock, Arkansas, United States of America.
authors.forename: Kenji
authors.initials: K
authors.lastname: Yoshigoe
authors.affiliation: Health Outcomes & Policy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Amanda
authors.initials: A
authors.lastname: Hicks
authors.affiliation: Department of Electrical, Computer, Software & Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida, United States of America.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Yuan
authors.affiliation: School of Information, Florida State University, Tallahassee, Florida, United States of America.
authors.forename: Zhe
authors.initials: Z
authors.lastname: He
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock, Little Rock, Arkansas, United States of America.
authors.forename: Mengjun
authors.initials: M
authors.lastname: Xie
authors.affiliation: Health Outcomes & Policy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Yi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Epidemiology, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.affiliation: Health Outcomes & Policy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Ramzi
authors.initials: R
authors.lastname: Salloum
authors.affiliation: Health Outcomes & Policy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: François
authors.initials: F
authors.lastname: Modave
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D061108:Social Media; D012938:Social Perception