社交平台與公開論壇中的迴聲室效應易造成討論參與者認知偏差，尤其當發生在
政治或時事議題討論時，更會造成極端主義以及社會分化的問題，也容易助長假新聞
之傳播。
本篇論文將以 Dcard 論壇上的時事、明星戲劇與成人內容等三大類貼文作為案
例，透過網路爬蟲、情感分析與語意分析等文字探勘技術，分析貼文與留言，藉由打
造一套人機介面資訊系統，輔助專家判讀對話健康程度與判斷迴聲室效應是否存在於
此。
透過案例探討 Dcard 這個年輕的網路論壇的環境與各個看板的現況。最後請 3 位
常於網路瀏覽貼文或參與討論的網路社群專家體驗本系統並給予回饋來改進本系統。

The echo chamber effect exists in social platforms and forums, will cause users stick
into cognitive bias. Especially when it comes to political or trending news, it will also cause
extremism, divide the people and help fake news spreading.
In this paper, we will build a post analyzer by web crawler, and some NLP tool like
sentiment analyzer, BERT model, and semantic cluster. The system will provide assistance,
assist the expert measure whether the conversation is healthy.
Then we will analysis these 3 topics: trending news, celebrities and adult content on
Dcard for case study. Through these cases, we can explore the environment of this fresh
online forum, Dcard, and the status of each sub forum. Finally, we interview 3 experts with
social media domain knowledge to experience the system and share their opinions with us to
improve the system.

論文審定書 i
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 ix
第一章 緒論 1
1.1 研究背景與動機 2
1.2 研究目的 5
第二章 文獻探討 7
2.1 同質性觀點 7
2.1.1 過濾氣泡 7
2.1.2 迴聲室效應 8
2.2 文字探勘 8
2.2.1 中文斷詞 9
2.2.2 情緒分析 9
2.2.3 文字向量 11
2.3 對話健康度 12
第三章 研究方法與步驟 14
3.1 系統架構 14
3.2 資料搜集 17
3.2.1 Dcard API 17
3.2.2 資料儲存 18
3.2.3 資料搜集腳本開發 19
3.2.4 選擇看板 22
3.3 資料分析 23
3.3.1 情緒分析 24
3.3.2 語意分群 26
3.4 資料呈現 32
3.5 深度訪談 37
第四章 分析結果 39
4.1 DCARD生態 39
4.2 案例分享 42
4.2.1 時事板 42
4.2.2 追星版 43
4.2.3 西斯版 49
第五章 結論 55
5.1 研究結論 55
5.2 未來展望 57
參考文獻 58
附錄I資料庫綱要 67
附錄II訪談大綱 71

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