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Distant reading case study "Miley Cyrus" ​

📖 I generally use this case study in my data feminism lecture to showcase possible feminist and intersectional readings.

Feminist and intersectional approaches to Miley Cyrus's VMA2013 performance ​

The VMA2013 controversy ​

Miley Cyrus's 2013 performance for the Video Music Awards (VMA) is still one of greatest social media events ever. Her controversial stage routine, characterised by sexually allusive dance moves and interactions with singer Robin Thicke, challenged traditional notions of family-friendly TV entertainment and female stardom, but also incited accusations of cultural appropriation and stereotyping. Miley Cyrus, who had become famous as a teen idol in the Disney Channel television series Hannah Montana (2006–2011), blurred the lines between childhood and adulthood in her performance. On the one hand, viewers embraced this as a statement of maturity that would free Miley from her former image. On the other hand, viewers negatively perceived the performance as another sexualisation of young women in the media. The intense debate that the performance sparked on social media but also in television shows and newspapers highlighted issues of gender, race, body image, artistic freedom, and cultural identity, making it an interesting case study for intersectional analysis.

Structural information ​

This repository provides a raw file with 100000 English-language tweets published immediately following the VMA performance. Ingest the data set into Voyant Tools and look at the word cloud. What words tell you something about the set up or structure of the data set? Watch out for the more "technical" terms to answer this question.

Main agents ​

Who are the main "agents" in the data set? Do you know anything about their relationship to each other, e.g. based on the video of the performance which you have watched at home? What is the agents' gender/ethnicity and why does it matter to the debate?

Analysing emotions ​

Which words say something about people's feelings? Are the feelings positive or negative? Note down the most important ones.

Keywords in context ​

Put some of the most prominent emotions into the context tool and check what triggered them. Are feelings associated with persons, or rather with what they do? Do any of the emotions expressed have a gender or race bias?

Put the words "black", "white", "family", "children" and "women" into the terms berry or links tool and find out what words most frequently co-occur. What does that say about common perceptions of gender roles and/or racial dynamics in the data set?

Put "black", "white", "dancers", "rape", "twerk*" and "women" into the trends tool. What does that tell you about the enfolding of the Twitter debate?