Abstract

Doping scandals can have a lasting effect on both athletes' and organizations' reputations, causing athletes to lose money and fans to lose trust in the organizations that govern their favorite sports. This paper investigates the impact a doping scandal can have on an athlete's reputation by  conducting a case study on Jannik Sinner, one of the top tennis players in the world, who ultimately accepted a suspension after testing positive for performance-enhancing drugs, despite being unintentionally exposed. Using data sourced from YouTube comments on tennis-related videos, a fine-tuned Mistral-7B-Instruct-v0.3 model with a classification head was used to carry out stance detection to investigate the stance of fans towards Sinner over time, classifying comments as guilty (believing Sinner intentionally cheated) or non guilty (supporting innocence, neutral, or irrelevant). Our model achieved strong performance on non guilty comments (F1=0.97) but only moderate precision and recall on guilty ones (0.71, 0.72), struggling with sarcasm, indirect accusations, and case-specific references. Analyzing 843,005 comments and focusing on those  spanning the 20 months post-scandal, the results show a downward trend in the percentage of guilty comments (Kendall's [tau] = -0.829), dropping from 2̃7.9% to 1̃.15%, supporting our hypothesis that doubters decrease over time. However, key events like Sinner's settlement and certain major  tournament appearances can temporarily reignite discussion. The results also confirmed that fans revisit older videos to discuss new doping allegations. These findings demonstrate that the effects  of a doping scandal slowly fade over time but persist for an extended period, and this paper offers a generalizable framework for examining similar cases across sports. This study highlights the potential value of social-media-based stance detection in informing anti-doping policies and communication strategies.

Library of Congress Subject Headings

Sinner, Jannik, 2001- --Public opinion--Data processing; Doping in sports--Public opinion--Data processing; Natural language processing (Computer science); Stance (Linguistics)--Data processing

Publication Date

5-6-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Software Engineering (MS)

Department, Program, or Center

Software Engineering, Department of

College

Golisano College of Computing and Information Sciences

Advisor

Ashique KhudaBukhsh

Advisor/Committee Member

Larry Kiser

Advisor/Committee Member

Christian Newman

Campus

RIT – Main Campus

Plan Codes

SOFTENG-MS

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