Twitter is the central platform involved in the process. Twitter API has three aggregated streams of Tweets, which are home timeline, user timeline, and mention timeline. The home time- line consists of retweets and the user's tweets, which represent the user's thoughts. Mimesis1.0 pulls tweets from the Gigi Hadid home timeline and then pipes tweets into a sentiment analysis model which will spit out 1,0,-1 as sentimental results meaning positive, neutral and negative. After that, it uses Processing to match these digits to green, red and blue and draws circles from inside to the outside. As a tree ring storing climate and atmospheric conditions data, Mime- sis1.0 stores personal sentiment data. It is a visual representation of the internet user. It is an avatar, as well as simulacra.

The goal of this website is to bring access to a broader range of audiences other than GitHub users. At the same time, it uses Mimesis as a medium to build a community that is also linked to a Twitter account.

The audience could input their information and submit their request in the 'view your Mimesis' section. Their Mimesis will be drawn and replace the placeholder. The new graph is also available for download. Their new requests will be updated in new blocks emerging after 'the latest request' block.

Special thanks to Benjamin Vu for helping composing javascript for this site.

Mimesis2.0 is the speculative result based on the same process as mimesis1.0, which is extracting data, conducting analysis, and drawing the result. The difference between 1.0 and 2.0 is not only replacing the sentiment model with the

cluster analysis mode

but also much higher complexity during the process. The MobileNet model in RunwayML(a democratized machine learning tool for artists and designers) is the one that I have used and tested for 2.0. Because this low fidelity model performs similar to the required cluster analysis model, which needs to be trained by enormous labeled datasets, the list of which will refer to the popular synsets in imagenet. After data is piped into the model, it generates several clusters. The names of these clusters are subsets of the labels in the data set, which will be

mapped to colors.

Cluster analysis model

Color matching

This personal report derives from the Mimesis1.0 functioning as further analysis of the Mimesis1.0 graph.

In RSPH's research report ‘Social Media and Young People's Mental Health and Wellbeing, five social media platforms, Youtube, Twitter, Facebook, Snapchat, and Instagram, are the primary targets in the survey, which is conducted among around 1,500 14 to 24-year-old people. Fourteen factors are being asked during the survey, including awareness, anxiety, loneliness, sleep, and self-expression. The result was Youtube ranked first and was the only positive platform among them all. Twitter ranked in 2nd place, then Facebook, Snapchat, and Instagram took last place.

At the same time, there is emerging research on the relationship between mental health and social media. 'We review recent work that utilizes social media "big data" in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.' Thus, this personal Twitter report might become auxiliary material for mental health analysis.

According to the trait of Twitter API and tweets object, there are two methods to filter tweets. The first method that twitter currently provides is seven parameters: name, count, since_id (from when), max_id (until when), trim_user, exclude_replies, and include_entities. When the request is submitted, these parameters help filter tweets. The second method is selecting the attributes in the tweet objects to write into a dictionary, and then filtering the dictionary for specific values of attributes. Filtering tweets according to different parameters might cater to different perspectives and needs.

The media uses representations - images, words, and characters or persona - to convey ideas and values. On different social media platforms, we use different packages of information to display ourselves and interact with each other. There are also auxiliary tools like avatars creating applications to recreate an animation yourself. But still, the process of creating these avatars is an accumulation of socially and historically constructed concepts. In other words, gender, skin color, race, body shape, and face shape are still ingrained in those avatars. Prejudices are built into existing institutions and structures, and even though they look like a new medium, however, they still create barriers and limit opportunities.

Mimesis 1.0 is a customized graph, generated from the text by sentiment analysis, model, and processing. Although there is bias ingrained in constructing a deep learning model, results could be a new form of identity representation. Is it more natural and neutral? Does it break barriers and limits?

Tinder users can choose to connect Mimesis like Snapchat and Instagram photos. From a user's perspective, bridging Snapchat and Instagram with Tinder increases the plurality of information, though it might turn into a concern for some users. Mimesis shares the same idea offering Tinder users an option to show more about themselves. On top of that, Mimesis provides an alternative for matching algorithms. In color practice, there are multiple choices to collocate different colors. What if matching algorithms could use Mimesis as a source to incorporate color combination theory to expand users' options? Users' cards will be pushed to others based not only on age, distance, and gender preferences but also on their Mimesis matches.

Platforms are places that will showcase your personality. There are a lot of behavior taxonomies out there. In Nancy White's research project: ‘Community Member Roles and Types,’ she came up with 15 archetype participation styles which are: core participants, readers/lurkers, dominators, linkers, flamers, actors, energy creatures, defenders, needlers, newbies, pollyAnnas, spammers, 'black & white' folks, 'shade of grey' folks and untouchable elders. Different archetypes maintain their style in multiple ways, including constructing a profile and engaging publicly in their own way.

In the book 'Virtual Social Identity and Consumer Behavior,' authors point out that the benchmark in adolescence is the kids’ desire and ability to explore multiple selves. It won't be a surprise that all internet users will not maintain the same identity through all the platforms. It's a fundamental desire to explore and embrace the other-selves within themselves. In everyday life, we approach every individual in a very different manner. Our behavior is based on the environment, who we are with, and what community we are in. This kind of behavior pattern can even be viewed on social platforms.

In Japanese, there is a phrase called '人設' (renshe) which originated from game design. It means the character design. Later on, in the Chinese internet environment, it became a phrase that is used to describe celebrities' public images. It is always designed by the company or celebrities' studio for marketing purposes. Gradually, every individual realized that they all have '人設' more or less. You might try to be tolerant, smart, or critical on Twitter. You might only post beautiful pictures on Instagram since it's visual-driven. The professional version of you will be on Linkedin.

There's a definite need here. What kind of application could help internet users build a better public image according to their needs? Currently, all different types of photo editing apps, beautifying filters, and avatar apps are prevailing. Most of these are visual-driven, targeting popular Instagram users. What’s next? What is the service for Twitter, and these text-driven platforms?

The Twitter bot has been published for almost a decade. The usage is still focused on research and some public services. 'The bot software may autonomously perform actions such as tweeting, re-tweeting, liking, following, unfollowing, or directly messaging other accounts. The automation of Twitter accounts is governed by a set of automation rules that outline proper and improper uses of automation.'

What if Mimesis is the automation rule that governs the bot? In that case, users could create certain types of Mimesis that follow specific behavioral patterns. In February, OpenAI published a language model called GPT-2 that generates coherent paragraphs of text one word at a time. Mimesis, GPT-2 model, and Twitter bot complete the critical components in the new application for Twitter.

Thomas Goetz mentioned that a feedback loop involves four distinct stages: evidence, relevance, consequence, and operation.From the user experience design perspective, it is not complicated to build a real feedback loop to counterbalance the dopamine-driven 'feedback loop.' It is possible to imagine a feedback loop where there is relevant evidence that can trigger users to adjust their subsequent behavioral. In the article ‘Harnessing the Power of Feedback Loop’, there is a case that city engineers slow down drivers in school zones by an average of 14 percent by merely showing current speed to the drivers on a dynamic speed display.

For Mimesis 2.0 and the ideal Mimesis, the speculative application happens on platforms functioning as user's information resources. In this scenario, the demonstration executes on Instagram's home page, which will be used as the background of the app. There are two different stages of the Mimesis. In the first stage, colors won't change but rather they’ll slowly float. The second stage will be triggered by the posts that are liked by the user. The new liked post will be analyzed by the system, and the analysis outcome will decide to increase, decrease or change colors. Referring back to the four stages of the feedback loop: evidence, relevance, consequence, and operation, Mimesis embedded in the background functions as the relevant evidence of the user's liked posts. When users’ likes are limited to a few categories of posts, the diversity of colors in Mimesis will diminish. This builds the consequence stage, which informs users of their 'bubble.' And finally, the 'bubble' nudges users to operate and completes the feedback loop.

What's more, this is not a speculation of what comes after dark mode design, but it could be an alternative option. Dark mode design has been criticized for not being proven to improve focus or productivity. A new design feature is a better definition of what it is. Mimesis background is designed as a feedback loop, as well as a customized background (for marketing).

The content moderator is the reverse engineering of the ideal mimesis. In this system, you have two slide bars to control your feeds. The variety of themes is linked to the diversity of the colors, and variety of perspectives is linked to the saturation of the colors.