Bachelor of Arts
Statistical and Data Sciences
National Football League, Sentiment analysis, Natural language processing, R, Python, Twitter, Multilevel modeling, Data ethics, Reproducible research
Following the actions of Colin Kapernick in August of 2016, incidents involving racially charged language towards NFL players became widespread across many forms of social media. Previous studies oﬀer qualitative evidence of racially disproportionate negative language and blame in sports contexts. We believe that this phenomenon is measureable on a larger scale. This thesis seeks to quantify previous research by ﬁrst accessing the Twitter API then running a sentiment analysis to determine the percentage of negative words for a subset of 24 NFL players at 5 time points. We ﬁt a multilevel regression model to test our hypothesis that during a game that was lost, sentiments will be more negative toward black players, whereas this diﬀerence will be smaller, or zero, in games that were won. Additional variables including yards and position were added to our model. Our results demonstrate that on average, the percentage of negative words during a loss decreases by 7.22 percentage points for white players relative to black players, holding position and yards constant. During a win, there is almost no diﬀerence in the observed sentiment between white and black players.
©2019 Julianna Grace Alvord Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Alvord, Julianna Grace, "Modeling racially disproportionate language on Twitter during NFL game play" (2019). Honors Project, Smith College, Northampton, MA.
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