期刊論文
| 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 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | EI ESCI NotTSSCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | DEU |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121629 ) |