Dataset Update & Further Development
After completing the third level of modeling, we decided to enriched our dataset (see 📁 Dataset chapter) — originally compiled through 10 real scenarios and a user story — by integrating key insights from the Portrayal + Perspectivisation ontology.
One of the most significant additions is the Character Type, a subclass of Cut, as it reflects how a character is positioned within the narrative, their importance, and the depth of their portrayal. These data points were considered highly valuable for constructing queries and extracting meaningful insights.
🔁 What Changed:
Adding Character Type column
To captures the narrative positioning, significance, and depth of each character.
It mproves the dataset's ability to support complex queries.
🔮 Further Development
This ontology can be extended by incorporating new relationships and integrating additional data from expanded datasets. Doing so would allow for a more nuanced analysis of LGBTQ+ character portrayal, its connection to societal values, and the ways in which different audiences perceive these representations.
An interesting avenue for future research could involve collecting data from audience reception, providing a broader perspective on how these portrayals are received and interpreted.
🔧 Dataset Extensions
New columns that reflect audience perception.
Include more characters and shows, especially from non-Western contexts.
👥 Audience Perception
Collecting data on how portrayals are received by different audiences could offer a broader perspective on the social impact of LGBTQ+ characters in teen media.
📌 These improvements could also power new research on:
Cultural bias
Cross-platform visibility
Real vs. perceived authenticity
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