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The International City Image of Beijing: A Quantitative Analysis Based on Twitter Texts from 2017–2021

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Listed:
  • Zhishuo Zhang

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Manting Luo

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Ziyu Luo

    (Institute of Art, Communication University of China, Beijing 100024, China)

  • Huayong Niu

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

Abstract

With the advent of the Internet era, users from numerous countries can express their opinions on social media platforms represented by Twitter. Unearthing people’s image perceptions of cities from tweets helps relevant organizations understand the image that cities present on mainstream social media and take targeted measures to shape a good international image, which can enhance international tourists’ willingness to travel and strengthen city’s tourism competitiveness. This paper collects nearly 130,000 tweets related to “Beijing” (“Peking”) from 2017–2021 through web-crawler technology, and uses Term Frequency-Inverse Document Frequency (TF-IDF) keywords statistics, Latent Dirichlet Allocation (LDA) topic mining, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis to further summarize the characteristics of Beijing’s international image and propose strategies to communicate its international image. This research aims to tap into the international image of Beijing presented on Twitter, and provide data support for the relevant Chinese and Beijing authorities to develop communication strategies, as well as providing a reference for other cities aiming to manage their international image.

Suggested Citation

  • Zhishuo Zhang & Manting Luo & Ziyu Luo & Huayong Niu, 2022. "The International City Image of Beijing: A Quantitative Analysis Based on Twitter Texts from 2017–2021," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10675-:d:899079
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    References listed on IDEAS

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