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

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.

City Image Research: Theoretical Frameworks, Perspectives, and MethodologiesA Citespace Bibliometric Analysis of SSCI Literature from 2013 to 2023

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Assessing Destination Brand Associations on Twitter: The case of Istanbul

“…In the case of machine learning-based methods, the system is trained with pre-labeled training data and emotion classification is performed with the trained system (Flores-Ruiz et al, 2021;Paolanti et al, 2021;Shimada et al, 2011;Viñán-Ludeña & de Campos, 2022). The dictionary-based approach relies on the sentiment dictionary, which is a set of known and precompiled sentimental terms (Becken et al, 2020;Zhang et al, 2022). The hybrid approach is based on a combination of a machine learning approach and a dictionary-based methods, which is used together with the sentiment dictionary in procedures (Claster et al, 2013).…”