Women’s Sport and the Media

We are witnessing a new age in regard of media coverage of women’s sport in the UK (Petty and Pope (2019), based on 2015 data). With that in mind, and knowing that, historically, women’s sport received between 5-10% the coverage of men’s sport (Bruce, 2013), it is timely to revisit contemporary research to identify what is currently happening in one particular media outlet – BBC Sport. To do this I have employed a digital social sciences perspective by relying on automatic data scraping of media reports using ‘if this then that’ (ifttt.com), and machine learning algorithms using rapidminer (rapidminer.com) to automatically classify the news based on the sport being reported.

Between 7th April and 17th June an applet I have developed on ifttt has automatically collated 1613 news items. After cleaning the data for the machine learning processes, I manually classified all 1613 news based on their gender. To do so I have followed those steps: if an athlete’s name is mentioned in the title, then this would imply a gender; if only an event’s name was mentioned where both women and men compete, then this would imply that both were reported; if no athlete’s name was mentioned in car or horse racing events, then this would imply those were car or horse – if a rider was mentioned than this would imply a gender.

In regards of the gender imbalance of reporting, only 233 were solely on women’s sport or female athletes. As such, women’s sport was represented in only 13% of this total of news items. To better visualise this as a timeline trend I have used Tableau (tableau.com) to shows peaks with women’s sport reporting. There were few; women’s sport reporting (green) tends to keep constantly around 13% of men’s sport reporting, while on some dates we can see peaks of reporting. For instance, on 18th April BBC Sport profiled the different athletes competing for the BBC Women’s Footballer of the Year. The peak on 23rd May was the announcement of those results; other peaks for 8th and 11th June were related to the FIFA 2019 Women’s World Cup in France.

With that in mind, I wanted to check if there were also imbalances in regards of the sport covered on both women and men’s sport. To do so I have applied a k-NN machine learning model, where I have manually classified a training sample of 403 (0.25), and the algorithm with a 93% accuracy automatically classified all the remaining news. In the visualization below, while football was the most covered sport overall, and most covered sport in absolute numbers for women’s sport (14%), it was only in tennis and athletics we could find some parity. This is congruent with the findings from Wanta (2009) and Coche and Tuggle (2018) who argue that when women’s sports are covered they tend to be on what they conceptualise as ‘acceptable sports’.

While this offers quantitative evidence, the findings somehow indicate a better panorama than that pictured by Toni Bruce in the early 2010s. Yet, a fine grain analysis on the quality of reporting or the sports being reported suggests this panorama has not improved significantly. Then, as now, ‘acceptable sports’ dominated, and some of the news focused more on what Luhmann (2000) and Rowe (2013) characterised as ‘scandals’ or stories that are not about the sporting performance (ie Caster Semenya’s court battle; how period affect athletes’ performances; and athletes’ private life).

If BBC Sport really strives for #changethegame as they claim (BBC, 2019), giving women’s sport equal ‘print’ coverage as well as air time makes sense. In future posts I will bring a cross-national comparison from media outlets in the USA and France.

*this blog first appeared on CarnegieXchange Blog (26/06/2019)

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s