IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i10p346-d1264730.html
   My bibliography  Save this article

A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023

Author

Listed:
  • Nirmalya Thakur

    (Department of Computer Science, Emory University, Atlanta, GA 30322, USA)

  • Kesha A. Patel

    (Department of Mathematics, Emory University, Atlanta, GA 30322, USA)

  • Audrey Poon

    (Department of Computer Science, Emory University, Atlanta, GA 30322, USA)

  • Rishika Shah

    (Department of Computer Science, Emory University, Atlanta, GA 30322, USA)

  • Nazif Azizi

    (Department of Computer Science, Emory University, Atlanta, GA 30322, USA)

  • Changhee Han

    (Department of Computer Science, Emory University, Atlanta, GA 30322, USA)

Abstract

Exoskeletons have emerged as a vital technology in the last decade and a half, with diverse use cases in different domains. Even though several works related to the analysis of Tweets about emerging technologies exist, none of those works have focused on the analysis of Tweets about exoskeletons. The work of this paper aims to address this research gap by presenting multiple novel findings from a comprehensive analysis of about 150,000 Tweets about exoskeletons posted between May 2017 and May 2023. First, findings from temporal analysis of these Tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of Tweets, the lowest number of Tweets, Tweets with the highest number of hashtags, and Tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of Tweets posted per hour and the different characteristics of these Tweets. Third, the paper presents a multiple linear regression model to predict the number of Tweets posted per hour in terms of these characteristics of Tweets. The R 2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech, #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these Tweets was performed, and the results show that the percentages of positive, neutral, and negative Tweets were 46.8%, 33.1%, and 20.1%, respectively. To add to this, in the Tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by sentiments of joy, disgust, sadness, fear, and anger, respectively. Furthermore, hashtag-specific sentiment analysis revealed several novel insights. For instance, for almost all the months in 2022, the usage of #ai in Tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, lexicon-based approaches were used to detect possibly sarcastic Tweets and Tweets that contained news, and the results are presented. Finally, a comparison of positive Tweets, negative Tweets, neutral Tweets, possibly sarcastic Tweets, and Tweets that contained news is presented in terms of the different characteristic properties of these Tweets. The findings reveal multiple novel insights related to the similarities, variations, and trends of character count, hashtag usage, and user mentions in such Tweets during this time range.

Suggested Citation

  • Nirmalya Thakur & Kesha A. Patel & Audrey Poon & Rishika Shah & Nazif Azizi & Changhee Han, 2023. "A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023," Future Internet, MDPI, vol. 15(10), pages 1-46, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:346-:d:1264730
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/10/346/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/10/346/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eszter Bokányi & Dániel Kondor & László Dobos & Tamás Sebők & József Stéger & István Csabai & Gábor Vattay, 2016. "Race, religion and the city: twitter word frequency patterns reveal dominant demographic dimensions in the United States," Palgrave Communications, Palgrave Macmillan, vol. 2(1), pages 1-9, December.
    2. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Patrizia Casadei & Neil Lee, 2020. "Global cities, creative industries and their representation on social media: A micro-data analysis of Twitter data on the fashion industry," Environment and Planning A, , vol. 52(6), pages 1195-1220, September.
    4. Stephan J. Goetz & Connor Heaton & Muhammad Imran & Yuxuan Pan & Zheng Tian & Claudia Schmidt & Umair Qazi & Ferda Ofli & Prasenjit Mitra, 2023. "Food insufficiency and Twitter emotions during a pandemic," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(2), pages 1189-1210, June.
    5. Chittaranjan Hota & Shambhu Upadhyaya & Jamal Nazzal Al-Karaki, 2015. "Advances in secure knowledge management in the big data era," Information Systems Frontiers, Springer, vol. 17(5), pages 983-986, October.
    6. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    7. Th'arsis Tuani Pinto Souza & Olga Kolchyna & Philip C. Treleaven & Tomaso Aste, 2015. "Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry," Papers 1507.00784, arXiv.org, revised Jul 2015.
    8. Amy Van Looy, 2022. "Definitions, Social Media Types, and Tools," Springer Texts in Business and Economics, in: Social Media Management, edition 2, chapter 2, pages 21-50, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlo Corradini & Emma Folmer & Anna Rebmann, 2022. "Listening to the buzz: Exploring the link between firm creation and regional innovative atmosphere as reflected by social media," Environment and Planning A, , vol. 54(2), pages 347-369, March.
    2. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 2017. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 19(2), pages 267-283, April.
    3. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    4. Ciprian-Octavian Truică & Elena-Simona Apostol, 2022. "MisRoBÆRTa: Transformers versus Misinformation," Mathematics, MDPI, vol. 10(4), pages 1-25, February.
    5. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    6. Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    7. Uğur Baloğlu, 2021. "Trolls, Pressure and Agenda: The discursive fight on Twitter in Turkey," Media and Communication, Cogitatio Press, vol. 9(4), pages 39-51.
    8. Xiaotong Sun & Charalampos Stasinakis & Georigios Sermpinis, 2022. "Decentralization illusion in Decentralized Finance: Evidence from tokenized voting in MakerDAO polls," Papers 2203.16612, arXiv.org, revised Mar 2023.
    9. Yan Mandy Dang & Yulei Gavin Zhang & James Morgan, 0. "Integrating switching costs to information systems adoption: An empirical study on learning management systems," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    10. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.
    11. Man-Fai Leung & Lewis Chan & Wai-Chak Hung & Siu-Fung Tsoi & Chun-Hin Lam & Yiu-Hang Cheng, 2023. "An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments," FinTech, MDPI, vol. 2(1), pages 1-17, March.
    12. Rocha, Augusto & Brown, Ross & Mawson, Suzanne, 2021. "Capturing conversations in entrepreneurial ecosystems," Research Policy, Elsevier, vol. 50(9).
    13. Mujtaba Ali Isani, 2021. "Methodological Problems of Using Arabic-Language Twitter as a Gauge for Arab Attitudes Toward Politics and Society," Contemporary Review of the Middle East, , vol. 8(1), pages 22-35, March.
    14. Till Blesik & Markus Bick & Tyge-F. Kummer, 2022. "A Conceptualisation of Crowd Knowledge," Information Systems Frontiers, Springer, vol. 24(5), pages 1647-1665, October.
    15. Nwaibeh, E.A. & Chikwendu, C.R., 2023. "A deterministic model of the spread of scam rumor and its numerical simulations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 111-129.
    16. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    17. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    18. A. V. Biju & Aparna Merin Mathew & P. P. Nithi Krishna & M. P. Akhil, 2022. "Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis," Digital Finance, Springer, vol. 4(4), pages 275-290, December.
    19. Ahmad H. Juma’h & Yazan Alnsour, 2018. "Using Social Media Analytics: The Effect of President Trump’s Tweets On Companies’ Performance," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 17(1), pages 100-121, March.
    20. Sven Gruener, 2024. "Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intelligence," Journal of Interdisciplinary Economics, , vol. 36(1), pages 58-78, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:346-:d:1264730. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.