Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts

By Jack Merullo, Luke Yeh, Abram Handler, Alvin Grissom II, Brendan T. O'Connor, Mohit Iyyer. 2019

23 citations recorded by the source corpus.

Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. Prior studies based on small datasets and manual coding show that such theatrics evince commentator bias in sports broadcasts. To examine this phenomenon, we assemble FOOTBALL, which contains 1,455 broadcast transcripts from American football games across six decades that are automatically annotated with 250K player mentions and linked with racial metadata. We identify major confounding factors for researchers examining racial bias in FOOTBALL, and perform a computational analysis that supports conclusions from prior social science studies.

Subfields: Artificial Intelligence, Economics and Econometrics, Safety, Robustness & Interpretability

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “In particular, they make predictions that directly reﬂect the harmful societal biases present in training datasets, such as racial bias in sports reports (Merullo et al., 2019) and political bias in news data (Fan et al., 2019).”
A Survey of Race, Racism, and Anti-Racism in NLP (2021, background): “(Merullo et al., 2019) or how words like ‘prejudice’ have changed meaning over time (Vylomova et al., 2019).”
Perturbation Augmentation for Fairer NLP (2022, background): “There is increasing evidence that models can instantiate social biases (Buolamwini and Gebru, 2018; Stock and Cissé, 2018; Fan et al., 2019; Merullo et al., 2019; Prates et al., 2020), often replicating or amplifying harmful statistical associations in their training data (Caliskan et al., 2017;…”
Gender-Aware Reinflection using Linguistically Enhanced Neural Models (2020, background): “…at a great pace and raised expectation about the quality of results and especially their impact in a social context, including not only race (Merullo et al., 2019) and politics (Fan et al., 2019), but also gender identities (Font and Costa-jussà, 2019; Dinan et al., 2019; Dinan et al.,…”