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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121629

    Title: Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining
    Authors: Yang, Yi;Hsu, Jia-Huey;Lofgren, Karl;Cho, Wonhyuk
    Keywords: Social media;Latent Dirichlet Allocation;text mining;machine learning;algorithm;Twitter;Weibo;social network analysis
    Date: 2021-08-14
    Issue Date: 2021-11-22 12:10:19 (UTC+8)
    Publisher: Springer
    Abstract: While the salience of social media platforms on modern interactive communication between diverse social actors has been demonstrated, less academic attention has been paid to comparisons between framed topics and user interactions across social media platforms, such as Twitter and Weibo. This article suggests text mining and natural language processing tools for cross-platform comparative social media studies, based on Latent Dirichlet Allocation (LDA) and social network analysis. This study illustrates how the suggested topic models and data processing algorithms can be applied to a real-life example (U.S.-China trade war discourse on social media), and experimented the methods on social media text mining data, revealing differences between user interactions on Twitter, predominantly ‘Western,’ and Weibo, largely representing Chinese-speaking users. We discuss the strengths and weaknesses of the suggested machine learning algorithms for comparative social media studies.
    Relation: Social Network Analysis and Mining 11, 75
    DOI: 10.1007/s13278-021-00772-w
    Appears in Collections:[Graduate Institute & Department of International Business] Journal Article

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