Social media analytics of the Internet of Things

被引:4
作者
Scheibmeir J.A. [1 ]
Malaiya Y.K. [2 ]
机构
[1] Systems Engineering Department, Colorado State University, 400 Isotope Dr, Fort Collins, 80523, CO
[2] Computer Science Department, Colorado State University, 1873 Campus Delivery, Fort Collins, 80523, CO
来源
Discover Internet of Things | 2021年 / 1卷 / 01期
关键词
Cybersecurity; Internet of Things; Machine learning; Popularity prediction; Sentiment analysis; Social media;
D O I
10.1007/s43926-021-00016-5
中图分类号
学科分类号
摘要
The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry. © The Author(s) 2021.
引用
收藏
相关论文
共 50 条
[1]  
Girma A., Analysis of security vulnerability and analytics of Internet of Things (IOT) platform, Information technology—new generations. Advances in intelligent systems and computing, (2018)
[2]  
Ashton K., That Internet of Things thing, RFiD J, 22, pp. 97-114, (2009)
[3]  
Shi W., Cao J., Zhang Q., Li Y., Xu L., Edge computing: vision and challenges, IEEE JIOT, 3, pp. 637-646, (2016)
[4]  
James J., Data Never Sleeps 2.0, (2014)
[5]  
Cruickshank I.J., Carley K.M., Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering, Appl Netw Sci, (2020)
[6]  
Guarino S., Trino N., Celestini A., Et al., Characterizing networks of propaganda on twitter: a case study, Appl Netw Sci, (2020)
[7]  
Tien J.H., Eisenberg M.C., Cherng S.T., Et al., Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership, Appl Netw Sci, (2017)
[8]  
Gomez-Garcia M., Matosas-Lopez L., Ruiz-Palmero J., Social networks use patterns among university youth: the validity and reliability of an updated measurement instrument, Sustainability, (2020)
[9]  
Bougie G., Starke J., Storey M., German D.M., Towards understanding twitter use in software engineering: Preliminary findings, ongoing challenges and future questions, Web2se '11, pp. 31-36, (2011)
[10]  
Williams A., Do software engineering practitioners cite research on software testing in their online articles? A preliminary survey, Proceedings of the 22Nd International Conference on Evaluation and Assessment in Software Engineering 2018 (EASE'18). ACM, pp. 151-156