Distant reading case study "Body Image"

🙌 This case study was inspired by MA DC student Caroline Déharbe, who used some of the data recommended below for her essay in the 2024/2025 academic year.

Reasons for studying body images in social media data

Body image is a relevant and much-discussed topic in our digital age as it influences self-worth, mental health, and societal expectations. Body images and their contestations can be analysed through different data, including podcast reviews and YouTube comments. When we analyse how audiences of audio and video content address body images online, we can find out about beauty standards and how they are confirmed or challenged by different demographics. Critical user feedback on beauty content may also highlight intersections between personal empowerment and collective activism.

Sources

For this case study, you may want to analyse reviews from two podcasts that directly engage with body image themes:

• I weigh -- with Jameela Jamil

  This podcast, hosted by actress and activist Jameela Jamil, explores mental health and body positivity. It features guests such as Esther Perel, Greta Thunberg, and Aubrey Gordon, who share stories that promote individual progress over perfection. Reviews of this podcast frequently discuss themes like vulnerability, empowerment, and societal change.

• We can do hard things This podcast does not have a narrow focus on body image but highlights (self-)love and overcoming difficult situations more generally. The podcast is hosted by author Glennon Doyle, her wife Abby Wambach, and her sister Amanda Doyle. The hosts encourage their listeners to "drop the fake and talk honestly about the hard things including sex, gender, parenting, blended families, bodies, anxiety, addiction, justice, boundaries, fun, quitting, overwhelm . . . all of it."

Alternatively of in comparison, you can analyse user comments for the following two YouTube videos:

UNDER CONSTRUCTION

Data analysis with Voyant

Use Voyant Tools for distant reading and identify key patterns in the data. As further explained in the Skills section of this Github repository, you should start with a word cloud / a high-level analysis of word frequencies. Recurring terms may include "empowerment" and "authenticity" but also emotions that podcast listeners and YouTube viewers express or judgments that they have experienced from others. Voyant’s contextual analysis tools (e.g. the keywords in context table) can help examine how prominent terms are used in relation to other words. Collocation tools like the word tree can reveal common phrases (e.g. adjective + noun combinations), while the trends graph can help you analyse the distribution of terms across the dataset. Also consider comparing themes between the two podcasts.

Possible theoretical approaches

To frame your findings, several theoretical approaches are possible. Based on what is taught in the MA DC course Machines of Knowledge, you may want to use one of the following three lenses:

• Feminism as a theoretical framework can help you reflect on women-led critiques of societal beauty standards as well as gender-related communication patterns and stereotypes. You can also consider what impact the gender identities of the content creators have on user reactions.

• Through a postcolonial approach, you can potentially examine if and why your data sets address cultural or racial dimensions of body image and self-love discussions. Consider how the user responses engage with global perspectives or marginalised voices.

• Public Sphere theory is another possible approach and can draw your attention to how media such as podcasts create participatory spaces and contribute to (political) opinion-making. You can, for example, explore whether reviews and comments reflect a shift toward inclusive and democratic conversations, and if the users are empowered to take collective action offline.