This thesis investigates the prevalence and discursive character of hateful, derogatory, and gendered commentary directed at female athletes online, focusing on comments posted beneath social media content on Instagram and YouTube concerning the Winter Olympic Games. While disproportionate online hostility toward female athletes is widely discussed anecdotally, it has received limited systematic empirical attention within a sport-specific context. This research addresses that gap through a mixed-methods approach grounded in Feminist Critical Discourse Analysis. The analysis proceeds in progressively narrowing stages: a quantitative macro-analysis of the most frequent adjectives, nouns, and verbs (via SketchEngine and custom categorization scripts); a micro-analysis of selected gendered lexical items' collocational behaviour (Word Sketch); and a close reading of n-grams and concordance lines interpreted through van Dijk's framework. The study concludes with a case study of athlete Alysa Liu, whose sudden rise in visibility illustrates the broader patterns identified. Findings show that hostile discourse is neither incidental nor random, but organized around recurring strategies: sexualization and objectification, delegitimization of achievement, infantilization relative to male athletes, and coded language evading moderation. This hostility further intersects with racialized and political discourse. The Alysa Liu case study extends these findings beyond text to non-consensual, at times AI-generated, sexual imagery circulating on a further platform, X (formerly Twitter). This thesis contributes a replicable analytical pipeline for studying online gendered hostility, documents automated moderation tools' limitations in detecting coded hate speech, and offers recommendations for platforms, sporting institutions, and future research.
Questa tesi indaga la prevalenza e le caratteristiche discorsive dei commenti offensivi, denigratori e connotati in modo misogino rivolti alle atlete online, concentrandosi sui commenti pubblicati sotto contenuti social media, più precisamente su Instagram e YouTube relativi alle Olimpiadi invernali. Sebbene l'ostilità online sproporzionata verso le atlete sia ampiamente discussa a livello aneddotico, essa ha ricevuto un'attenzione empirica sistematica limitata in un contesto specificamente sportivo. Questa ricerca colma tale lacuna attraverso un approccio a metodi misti fondato sulla Feminist Critical Discourse Analysis. L'analisi procede attraverso fasi progressivamente più mirate: una macro-analisi quantitativa degli aggettivi, sostantivi e verbi più frequenti (tramite SketchEngine e script di categorizzazione personalizzati); una micro-analisi del comportamento collocazionale di specifici elementi lessicali genere-specifici (Word Sketch); e una lettura ravvicinata di n-grammi e linee di concordanza interpretate attraverso il framework di van Dijk. Lo studio si conclude con un caso di studio sull'atleta Alysa Liu, la cui improvvisa visibilità illustra i pattern più ampi identificati. I risultati mostrano che il discorso ostile non è né accidentale né casuale, ma organizzato attorno a strategie ricorrenti: sessualizzazione e oggettivazione, delegittimazione dei risultati sportivi, infantilizzazione rispetto agli atleti uomini, e linguaggio codificato per eludere la moderazione. Questa ostilità si intreccia inoltre con discorsi apertamente razzisti e politici. Il caso di studio su Alysa Liu estende questi risultati oltre il testo, includendo immagini sessuali non consensuali, in alcuni casi generate tramite intelligenza artificiale, in circolazione su un'ulteriore piattaforma, X (in precedenza Twitter). Questa tesi contribuisce con una pipeline analitica replicabile per lo studio dell'ostilità genere specifica dei social media, documenta i limiti degli strumenti di moderazione automatizzata nel rilevare hate speech codificato, e offre raccomandazioni per piattaforme, istituzioni sportive e ricerche future.
Beyond the Podium: A Critical Discourse Analysis of Misogynistic Hate Speech in Milano-Cortina 2026 Online Commentaries
MANFREDINI, AMANDA
2025/2026
Abstract
This thesis investigates the prevalence and discursive character of hateful, derogatory, and gendered commentary directed at female athletes online, focusing on comments posted beneath social media content on Instagram and YouTube concerning the Winter Olympic Games. While disproportionate online hostility toward female athletes is widely discussed anecdotally, it has received limited systematic empirical attention within a sport-specific context. This research addresses that gap through a mixed-methods approach grounded in Feminist Critical Discourse Analysis. The analysis proceeds in progressively narrowing stages: a quantitative macro-analysis of the most frequent adjectives, nouns, and verbs (via SketchEngine and custom categorization scripts); a micro-analysis of selected gendered lexical items' collocational behaviour (Word Sketch); and a close reading of n-grams and concordance lines interpreted through van Dijk's framework. The study concludes with a case study of athlete Alysa Liu, whose sudden rise in visibility illustrates the broader patterns identified. Findings show that hostile discourse is neither incidental nor random, but organized around recurring strategies: sexualization and objectification, delegitimization of achievement, infantilization relative to male athletes, and coded language evading moderation. This hostility further intersects with racialized and political discourse. The Alysa Liu case study extends these findings beyond text to non-consensual, at times AI-generated, sexual imagery circulating on a further platform, X (formerly Twitter). This thesis contributes a replicable analytical pipeline for studying online gendered hostility, documents automated moderation tools' limitations in detecting coded hate speech, and offers recommendations for platforms, sporting institutions, and future research.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14239/37085