Project #10. Translation Device

link to p5.js sketch

Design Process

I started off by searching APIs on Google. After about two hours of searching, I decided to go with League of Legends, a game I've been playing and enjoying for several years. I wanted to generate an in-game statistics device like OP.GG but there was a problem while I was trying to query the API into p5.js.

So I ended up using Wordnik, a thesaurus API, to analyze a JSON file of League of Legends. In the past years of playing LoL, I've noticed the number of female players of League of Legends is way less than that of male players, so I was wondering if I could get into the reason of this circumstance.

Since I couldn't get the information of each player, I focused on the difference between female-featured characters and male-featured characters by researching their illustration, position, and blurbs. I could easily figure that female characters are illustrated with more body exposure and idealized beauty standards. While that was very obvious, I was curious if there's any biase in blurbs of each character.

Description

This project shows lists of synonyms of four words on idex: evil, good, seductive, pristine. The illustrations of champions are located based on the context of words which are used in their character blurbs. There are 5 female-featured characters and 5 male-featured characters that I randomly chose from 140 LoL characters.

Reflection

I mainly focused on the principle #6 Consider Context to understand the behind scene of the desgin process of each game character. While I was working on this project, I definately see the trend that female-featured characters are tend to be described in evil and, especially, seductive way in comparison to the male-featured characters.

I hope this project adds another step before the game players uncounsciously pick up the biases from the game design. I'm looking forward to expanding this project with the image files of characters illustrations, like getting the pixel data of each image and studying how many skin-tone data are more involved in female-featured characters than male-featured characters.