教師資料查詢 | 類別: 期刊論文 | 教師: 許佳惠HSU JIA HUEY (瀏覽個人網頁)

標題:Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining
學年110
學期1
出版(發表)日期2021/08/14
作品名稱Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining
作品名稱(其他語言)
著者Yi Yang; Jia-Huey Hsu; Karl Lofgren; Wonhyuk Cho
單位
出版者
著錄名稱、卷期、頁數Social Network Analysis and Mining 11, 75
摘要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.
關鍵字Social media;Latent Dirichlet Allocation;text mining;machine learning;algorithm;Twitter;Weibo;social network analysis
語言英文
ISSN
期刊性質國外
收錄於EI;ESCI;
產學合作
通訊作者
審稿制度
國別德國
公開徵稿
出版型式,電子版
相關連結
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