Summarisation of our topic evaluation and working progress towards the first group deliverable
Introduction
As part of the inter-university course Data Physicalization, we consist, as group 3, of Anjali & Simisola from Bauhaus University Weimar, Sebastian & Maximilian from LMU Munich and Dominik from the University of Augsburg.
In our collaborative work environment, we use Whatsapp and Zoom for communication and our weekly meetings. We also work with Miro and Google Services to create content.
Topic Research
We have started to identify different contexts and the hidden data in them. Subsequently, from the identified proposals that we captured, we decided to look more closely at the context of “public space”, as it felt like a promising area to work within this course.
We intended to visualise the routes taken by pedestrians to help others find places of interest since the movement of people and the cumulation of their gathering can be seen as hidden data.
However, we have concluded that our intention and the information we wanted to visualise offers no added value.
Therefore, as a group, we started another brainstorming round to find a more suitable scenario or context to work with.
Topic Re-Orientation and Elaboration
The outcome of our group discussion on a suitable topic led to the decision to shift the context of consideration to relationships and friendships. Our goal was to find the hidden data that lies in the emotions we have towards other people. Therefore, we agreed on the scenario that we use chat messages as our data source and, most importantly, the emojis used in these chats to analyse specific aspects of interpersonal relationships.
As we wanted to remain open and did not want to commit ourselves at this early stage to how we should treat the subject, we kept the tracking and design process very free so that each member of the group could explore for themselves.
With this in mind, each of us created a postcard that can be seen in the following:
Sebastian Burgkart:
Sebastian Burgkart: My data source was every chat message I wrote to multiple friends over the course of one week. The grey vertical bars divide the timeline into days (going from left to right). The colour/symbol on the left side groups emojis into four different categories: Love/Trust, Fun/Funny, Happy/Content and Embarrassed. More dots represent multiple emojis used (e.g. the last day was a very loving day). Overall it is like a weekly mood board that represents my days incredibly well.
Maximilian Rauh:
As my data source, I chose one chat history with one person over one year. It starts in the centre at the beginning of 2019 and ends with new years eve.
I summarized all love related emojis and encoded their amount for each month. The dots on the left side of the line represent steps of tenth in the decimal system, and the dots on the right side represent single integer steps.
For example, in September, an amount of 12 love-related emojis were sent to each other in the chat.
Simisola Aremo:
On the card, I drew emojis, each with a supporting text. The messages chosen were only the type usually sent to a friend or a familiar person. The goal was to visualize these messages and understand whether the receiver correctly interpreted the intention of the message. This is to learn whether these selected emojis can be misinterpreted when used with people we are not in a relationship or friendly with.
Anjali Pawar:
My postcard is basically a letter about an invitation. It’s a combination of both emojis and words in order to convey the message. So I have tried to use these emojis as part of the situation within the letter so as to express love/gratitude and information such as birthday, time, music.
Dominik Köfer:
For my postcard, I tracked the emojis I used in most Whatsapp-Chats for one week. I didn’t include all of my group chats. Every Emoji on the postcard represents one of four categories. They are from left to right: friendly/happy, laughing, love and awkward/embarrassed. For each emoji, there is a main branch and little branches attached to it. One little branch stands for one day and every line coming out of it means one emoji was used out of this category. What stood out for me during this week of tracking is, that I used laughing-emojis pretty often, even when they weren’t the perfect fit for the current conversation.
Insights and Future Work
In the following section, learnings and insights of the initial exploration phase will be presented. Especially the creation process and discussion of the postcards led to a more precise prototype and a clearer understanding of what we want to achieve with our Physicalization.
Summarized Persona
Tom is 23 years old and a student at the Ludwig Maximilian University of Munich. He is also employed as a working student in an international IT-Company. He has no girlfriend and, therefore, really cares about being in a social circle. However, more often than not, he is so stressed with his 20-hour job and university that he forgets to write to his friends that are truly important to him. He wants a visual way that reminds him of his communication/emotional status to his best friend.
Data Input
WhatsApp is in Germany one of the main messaging services to stay in contact with one another. Therefore this chat data is used to cover a wide array of friendships and social connections. Additionally, it is possible to export this data in a format which can be used for further processing.
Hidden Data / Process
Our Data-Processing has two steps, which both uncover hidden data. Firstly, the hidden data of emojis is analyzed in detail. For example, these two emojis ☺️😊 look similar but could be used differently. Their meaning could also vary from person to person. One approach would be to empirically collect our use of certain emojis and their respective emotion. It might be suitable to focus on the six base emotions: anger, disgust, fear, happiness, sadness and surprise [1]. Then emojis are sorted according to the strength of the emotion they are representing (e.g. this emotion is sadder 😭 than this emoji 😢).
The second hidden data source is the friendship-activity level (could be compared to Trust and Love). Here the comparison to other chats and your previous chat behaviour with this person, combined with the emojis data, is used to create a score of how close you are with the person at the moment. E.g. if you have not written in a long time, this score will go down.
Data Output / Physicalization / Visualization
Our current idea is to use this data to create a Friendship-Stone. Here the colour, the temperature, the texture, the size and the form give visual and haptic indications of how well you are currently connected to a friend. This can act as a reminder to write a person again or change the subject if you are only fighting in chat.
[1] https://www.verywellmind.com/an-overview-of-the-types-of-emotions-4163976, accessed: 21.11.21