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Research Synthesis and Thematic Analysis of Twitter Through Bibliometric Analysis

Author

Listed:
  • Saleha Noor

    (School of Information Science and Engineering, East China Science and Technology University, China)

  • Yi Guo

    (School of Information Science and Engineering, East China University of Science and Technology, China)

  • Syed Hamad Hassan Shah

    (Glorious Sun School of Business and Management, Donghua University, China)

  • M. Saqib Nawaz

    (School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China)

  • Atif Saleem Butt

    (American University of Ras Al Khaimah, UAE)

Abstract

In literature, there is a shortage of comprehensive documents that can provide proper details about Twitter in research community. This study conducted a first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries on Twitter. Similarly, bibliometric mapping analysis is carried out to explore different research themes in Twitter publications. VOSviewer was employed to process the 11,006 Twitter publications retrieved from the Web of Science (WoS) from 2009 to 2018. Obtained results suggest that USA and China received the highest number of publications on Twitter research, while the University of Illinois was the most productive institute. Furthermore, the five major themes have emerged in Twitter publications, and its remarkable role has been found in event detection, sentiment analysis, education, health, politics, and crisis as well as risk management. The authors believe that this study will open new doors for researchers to use online Twitter social networking communities in beauty salons, consulting companies, banks, and airlines.

Suggested Citation

  • Saleha Noor & Yi Guo & Syed Hamad Hassan Shah & M. Saqib Nawaz & Atif Saleem Butt, 2020. "Research Synthesis and Thematic Analysis of Twitter Through Bibliometric Analysis," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 16(3), pages 88-109, July.
    • Handle: RePEc:igg:jswis0:v:16:y:2020:i:3:p:88-109
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    Cited by:

    1. Yu, Qinyao, 2022. "Simulation of the interactive prediction of contemporary social change and religious socialization based on big data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Huang, Anzhong & Xu, Rui & Chen, Yu & Guo, Meiwen, 2023. "Research on multi-label user classification of social media based on ML-KNN algorithm," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Ali, Mohsan & Hassan, Mehdi & Kifayat, Kashif & Kim, Jin Young & Hakak, Saqib & Khan, Muhammad Khurram, 2023. "Social media content classification and community detection using deep learning and graph analytics," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

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