本論文探究美國電視影集《無恥之徒》劇本對話中仇恨言論（Hate Speech）的實際運用模式，檢視了仇恨言論的組成元素、子類別、以及顯性與隱性的表達形式。本論文整合了Bisht等人於2020年提出的仇恨言論子類型（Subtypes）與Lewandowska-Tomaszczyk 等人於2023年提出的冒犯性語言層面（Aspects），以回答三個研究問題：第一、在該影集中，哪些子類型出現與共現的頻率最高？第二、在該影集中，哪些層面出現與共現的頻率最高？第三、根據該影集中出現的仇恨言論，有哪些額外的分類方式能使現有的仇恨言論分類更加完整。在研究方法上，本論文將該影集全11季共134集的完整劇本作為研究來源，並使用自動化關鍵詞檢索與人工手動識別的混合方法進行收集與編碼，最終成功識別出1,344筆仇恨言論實例，並利用描述性統計與推論性統計（如卡方檢定）等進行量化分析。量化結果顯示，在八個既有子類型中，針對性別的性別歧視（sexism）以高達54.6%的比例成為最主要的仇恨表達形式，其次則是針對身心障礙（disability）與出身地（origin）的言論。而在共現模式上，雖然性別歧視與身體（body）的共現最為頻繁，但其統計上的關聯性並不顯著（p>0.05），而性別歧視與出身地以及種族歧視（racism）與出身地兩組子類型則呈現出顯著的統計關聯（p<0.05）。另一方面，針對仇恨言論的七個層面，分析結果表明敵對性（hostile）與損害名譽（discredit）的出現頻率均超過九成，且敵對性與損害名及前兩者加上仇視性（hateful）的組合為最普遍的共現模式。此外，本研究亦依據影集中出現的仇恨言論，提出了針對階級（class）與威脅（threat）的子類型，以補足現有子類型的缺漏之處。並同時根據仇恨言論的詞性與功能，將其表達形式進一步細分為指涉性（referential expression）、描述性（descriptive expression）與行為性（behavioral expression）三種。本研究在仇恨言論領域上的貢獻包含結合自動化與手動分析以蒐集影集中的仇恨言論、將仇恨言論顯性與隱性的表達形式與不同的分類框架相結合、並延伸發展當前的分類體系，使之更加完整。未來研究可進一步比較《無恥之徒》的美國版與英國版影集，以深入探討文化差異對於仇恨言論造成的影響。

This thesis explores the manifestation of hate speech in the dialogue of the American television series Shameless. In order to address the limited research on hate speech in the field of movies and TV series, this study integrates two classification systems: the subtype categories (Bisht et al., 2020) which classify hate speech based on the themes and targets, and the aspect Offensive Language Taxonomy (Lewandowska-Tomaszczyk et al., 2023) which categorizes the elements that make up hate speech. This thesis aims at answering three research questions: (1) What kind of hate speech (Bisht et al., 2020) are most frequently represented and co-occur in Shameless? (2) What aspects of hate speech (Lewandowska-Tomaszczyk et al., 2023) occur and co-occur most commonly in Shameless? (3) What additional subtypes can be added into the current eight subtypes?
In this study, automatic keyword detection and manual identification are used to collect data from the 134 episodes across 11 seasons. The data are then analyzed using quantitative involving descriptive and inferential statistics as well as qualitative methods. The results show that hate speech related to sexism, disability, and origin show up the most commonly in Shameless, while the aspects of hostile and discredit are the most frequently covered. In terms of their co-occurrence, sexism and body, sexism and origin, as well as racism and origin are pairs that co-occur the most. On the other hand, the most salient co-occurrence patterns of aspects involved the combined presence of hostile, discredit and hateful. Hate speech keywords targeting female and male characters in Shameless are also found different and can be classified into different layers. Furthermore, some additional classifications are found to further categorize hate speech, e.g., those related to target individuals’ socioeconomic status and those used to threaten them. On the other hand, hate speech emerges as distinct expressions (e.g., referential, descriptive, and behavioral) as well based on the parts of speech. By examining data in Shameless, this thesis proposes the importance of adopting a multi-layered classification to analyze the explicit and implicit expressions, as well as proposing different classification of hate speech.

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