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  • Gabriella H Axelson

How do people engage with female athlete centred ads compared to male centred ads?

For one of my classes for my master's degree, I had to create a portfolio where I applied some of the research methods I had learned to answer a question. For this particular question, I used the research method of social listening to see what similarities and differences could be found in ways one engaged with female athletes and male athletes. To do so, I looked at Nike’s Dream Crazy (a male-athlete centered ad) and Dream Crazier (a female-athlete centered ad) on YouTube and Twitter. Since these advertisements were released a while ago (September 2018 and February 2019 respectively), I looked at the contents of the comments from the past month only.

The reason I chose this method was because it would allow me to analyse the comments and categorize it in a way that could then be formatted visually to show what people were saying, and at what quantity the content was being said. This analysis and visual representation of the findings is key to successfully showing not just what was being said, but how the audience was engaging—it showed how individuals were engaging positively or negatively.

I collected data from the comments by grouping them into categories such as: love (this ad); boycott (Nike); Kaepernick neg.; Williams neg.; inspiration; stupid. I chose these categories as they categories as they still maintained the positive or negative connotation of the comment, but was shorted in a way that could be easily grouped with similar comments, despite those comments not using the same exact words/wording. Once all of the comments for the past month were categorized, I used RawGraphs’ Dorling map generator to create a visual representation of my findings.

During the analysis process of the comments, I found that it became increasingly difficult to try and sort each individual comments into their designated category. For example, there were many comments that I interpreted to be sarcastic, so I would try to analyse whether or not I should categorize the comment based solely on words used, or based on connotation. In the end I decided categorize comments such as these based on connotation (and other audience engagement), as I was questioning how the audience engagement differed between the two advertisements, not questioning what was being said. Another example of difficulties I found were categorizing memes and gifs. There were some that used them, but this seemed to offer no valuable meaning to the analysis and were unrelated to the comment thread.

It is also important to note that, for this research method, I did not categorize the audience members who commented. I mention this because, while reading through the comments, I found that there seemed to be a correlation between negative comments for Dream Crazier and male accounts. In the future, this is something I will keep in mind, as it would offer a more in depth understanding of who the audience members are, and what their beliefs are—not just what they are commenting.

In the end, I found that the biggest commonality for both advertisements was that people would comment about how Nike uses sweat-shops, under-pays their employees, or do not employee enough diverse people (this was categorized as ‘better work conditions for employees’). One of the most interesting finds, however, were how different the comments were on YouTube compared to Twitter—YouTube featured more negative comments for both ads, were as Twitter overall had more neutral or positive comments.

Appendix A: Dream Crazier (female athlete) YouTube Comments

Appendix B: Dream Crazier (female athlete) Twitter Comments

Appendix C: Dream Crazy (male athlete) YouTube Comments

Appendix D: Dream Crazy (male athlete) Twitter Comments

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