因為網路匿名化的特性，越來越多霸凌行為容易透過網際網路作為傳遞的媒介，在網際網路上惡意地傳播著，由於其造成的效果會對社會帶來許多改變，許多研究開始探討網路霸凌的成因及特徵，以提供閱讀者以及網站經營者及早過濾不當的霸凌文章使用。

過去網路霸凌的言論多半圍繞著宗教、政治，隨著近年來女性主義的興起，越來越多關於仇恨女性的網路霸凌言語，也開始頻繁地出現在網路上，PTT 論壇甚至在 2015年時出現母豬教等詞彙，以仇恨女性當作信仰，在網路上針對女性進行無差別攻擊，故本文想了解網路霸凌中，以仇恨女性為主體的厭女風氣在網路上的特徵以及成因。與傳統的質性研究方法不同，本論文中將使用文字探勘的方式，能夠在短時間內，透過將爬蟲自動讀取台灣網路論壇 PTT 大量的語料進行特徵分析，並以分群等演算法將語料進行主題識別。接著以序列分析法尋找不同時段厭女之網路輿論與網路霸凌等關聯，最後透過機器學習演算法，對比不同的參數設置來提升演算法對於文字辨別的程度，並輔以文字雲將不同時段的網路留言視覺化，以此比較 PTT 不同看版之間針對厭女風氣成因及特徵之探討。

Because of the anonymity of the Internet, more and more bullying behaviors are spreading maliciously through the Internet as a medium of transmission. Due to its effects will have a negative impact on society, more and more studies have begun to discuss the causes and characteristics of cyberbullying, so as to provide readers and website operators with early filtering of inappropriate bullying articles for use.

In the past, most cyberbullying remarks revolved around religion and politics. With the rise of feminism in recent years, more and more cyberbullying comments about misogyny have also begun to appear frequently on the Internet. In 2015, PTT forum appeared with a word called sow religion, it regarded the hatred of women as a belief, and indiscriminate attacks against women were carried out on the Internet. Therefore, this article wants to understand cyberbullying and misogyny.

In this paper, the text mining method will be used to analyze the characteristics by automatically reading a large amount of the corpus of Taiwan Internet Forum PTT, and to identify the subject of the corpus with algorithms such as clustering. Then, the sequence analysis method is used to find the relationship between online public opinion of misogyny and cyberbullying at different times. Finally, the machine learning algorithm is used to compare different parameter settings to improve the algorithm's degree of text recognition. In order to compare the causes and characteristics of misogyny between different versions of PTT, and different time periods messages of visualization will be presented as a word cloud.

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