Digital Body Language
Digital written text surrounds our everyday life through various mediums such as chat messages, articles, and books etc. While text is being convenient for a myriad of task, it often doesn’t reveal a lot about the process of its creation. It also lacks a lot of visual information when compared to e.g voice and real face to face interaction. When communicating through text, we are left with only the words and the known context to make meaning.
This makes it harder to convey e.g emotions through text. Smileys, emoticons and memes can be seen as tools to help overcome this problem and add additional meaning to a message beyond the words. However, while the use of these tools is an active choice from the user, they can’t manifest the same kind of authentic information that gesture and body language can in a face-to-face conversation.
Digital Body Language is a text processor created to explore how digital text can visually convey more of the users personal writing style and emotional state, as a part of the static text. Inspired by gestures from face-to-face interaction, we have explored abstractions of typing gesture within digital text.
When the user writes a text inside DBL, information about the user’s writing style and flow is measured. In real-time this data is used to affect the user’s text output as a way to reveal information about the user’s writing process and make the user’s “gesture” present through the typography.
1. How long pauses does the user have between two words. Visual this creates a bigger space between the words controlled by the length of the pause in time intervals between 0,5 – 2 seconds. This effect was interpreted by us as way to show how confident a user is about what to write, or whenever the user needs time to reflect upon how to continue the message.
2. How long pauses does the user have inside an unfinished word. While the word is being written, this pause determines the color of the word in gray-scale, where total black is a pause of 2 seconds or longer. This is a way to show if the user was reflecting on the use of the current word – by making it visual stronger.
3. How fast is the user writing a word. This rule affects the tracking of the word, being the distance between each letter. The faster the word is typed, the tighter the tracking is. This is a way for us to show the user’s tempo and intensity.
4. How many words is the user deleting. The amount of words the user deletes in a row, determines the size of the next word being typed. The more words deleted, the bigger font size is applied. This effect can be seen as a way to visualize words which have been replacing one or more words. It’s Interpreted by us as a sign of an errors you wouldn’t have been able to take back unnoticed if it was a face-to-face conversation.
This Project was created in collaboration with
Bianca Di Giovanni
Tools used: Processing