Painful Objects Proposal

Data Art


A few weeks ago I walked around my apartment and recorded all the objects that at one or multiple points had caused me physical harm. I was interested at seeing how many hazardous objects there were, where these things were located, and ranking the the amount of pain they caused. Most of all, I wanted to write about the experience of getting betrayed by stuff that was supposed to be on my side (though I should probably resist the urge to anthropomorphize, I never seem to be able to).

For me, there was something cathartic about recording these painful experiences. So my proposal is to create an online space where anyone can contribute their objects, and view all the objects that have been submitted.

Visual Representation: 

The basic concept is to depict red droplets within a frame. The frame represents the home, and the droplets represent the objects/experience of pain. Here are the variables I want to use:

Size of droplet = # of painful incidences
Saturation of droplet = amount of pain from 1 to 10
Bluish hue = if there was emotional pain and how much (from 1 to 10)
Droplet position = clustered based on location of object in home
Mouse position = Hover over droplet to reveal information about the object

I’m most conflicted about how to represent the “home” and where to place the droplets within the home. This is because everyone’s home looks different! Should I draw lines to demarcate rooms, or is that too literal? Should I make it look like a blueprint, or is that way too literal? For the time being, I’m modeling the home based off of my own apartment (since that’s the only data set I have so far).


I created a few drawings in Illustrator to brainstorm what the web page might look like:

I also made a quick p5 sketch of a potential droplet:

I decided to make a physical representation as well. I used a mug of water and a plastic chopstick to collect water droplets.  I put droplets of water on a piece of paper (1 droplet = 1 incident, 2 droplets = 2 incidents, and so on) and then I dipped a red pen into those droplets (1 dip = 1 on pain scale, 2 dips = 2 on pain scale, etc.) Unfortunately the first couple of droplets sapped nearly all the ink out of the pen, so the saturation is too weak overall. I thought I was very clever with my chopstick method but I should’ve just picked up some cheap watercolors and brushes from a drug store. Here’s the result:


If you’d like to add to my dataset, here’s the form!

Zora’s Astonishing Circus Acts: A Storybook Game

Designing Games for Kids

Assignment: Make a game for little kids (ages 4-6)

Concept: Write a children’s book that has a game embedded into the story.

Research: The first thing that I did was look up other children’s books that had a similar concept. I found a lot of books that had clever lift-the-flap mechanisms:

Dear Zoo by Rod Campbell

Paper ZOO by Marya Dzianová’s

I found other books that had different kinds of animation, like Emily Cedar’s “What Makes the World go ‘Round”:

I also found out about the incredible Hervé Tullet, who is known in France as “The Prince of Pre-school books”. Two examples seen below are The Finger Circus Game and The Game of Shadows:

Because I’m not a visual artist, I didn’t want the game to be based on a clever visual or tactile effect. Instead, I wanted to think of a way to get the children playacting and have some agency over the story.

Refined Concept: I realized that kids love pretending to be animals. So I decided that the main game mechanic would be having kids act out animals doing silly things. I made a long list of animals that I thought young children would recognize and also enjoy mimicking–like jellyfish, shark, grizzly bear, dog and chicken. Then I made a list of actions that included surfing, playing guitar and practicing karate. I used a variety of verbs (instead of just “doing”) in order to make it more educational.

The next step was figuring out what kind of story would facilitate that kind of playacting. Perhaps with Herve Tullet’s Finger Circus fresh in my mind, I realized that a circus would be the perfect place for these animals to perform.

Fabrication: The story has gone through three drafts and two physical prototypes. I created two decks of cards for the animals and the actions that the children would draw at random on their turn. Because I used 20 animals and 20 actions, that means there are 400 possible combinations!

The first prototype used velcro, which made it really easy for the kids to stick their cards directly onto the page of the book. Unfortunately, it made it impossible to stack the cards in decks, and also made the book bulkier. For my second prototype I decided to use plastic sleeves that the kids could slide the cards into, but that made it much harder for the kids to place the cards. I think the plastic sleeve is more elegant than velcro but the design needs some refinement.

Playtesting: So far I have playtested the book twice. Once with my professors kids (who are ages 4 and 8) and with a whole classroom of preschoolers. My professor’s kids really enjoyed it (and they loved goading their dad into acting out animals as well), and it was a huge hit in the classroom! The kids were rapt the entire time, even though my book doesn’t really have illustrations yet (the only illustrations I have are of the different animals). A few of the kids who didn’t get a turn wouldn’t let me leave the classroom until I promised that they would have a turn next time! I have a few other appointments to visit classrooms so hopefully I will be able to get permission to snag a picture and post it here.

Demo: Finally, here is a short video demo. Fortunately the kids are much better at being animals than I am.

Mushroom Death Remediation

Fungus Among Us

A few weeks ago I watched this TedTalk given by Jae Rhim Lee:

Her message really resonated with me. I understand the temptation of family members to try to preserve the bodies of loved ones after death, but for my part, I don’t want anyone looking at me after I’m dead, making comments how good I look (considering the circumstances), and admiring what a good job the mortician did. I don’t want my body tossed into the Earth like a leaky formaldehyde bag wearing lipstick. My grandmother’s wake was horrible for me. She didn’t look like herself at all. It was like we were burying a stranger.

Jae Rhim Lee created a mushroom burial suit. It costs much less than the average casket, and it’s far better for the environment. Lee has trained fungus to get good at eating what will be her remains by feeding it her skin cells, hair, nails, sweat, and blood. Even though it might seem morbid to teach an organism to eat your body, I thought there was something poetic about it, and it inspired me to write this short science fiction piece:

A way in which I might extend this project is to have a podcast that features a science fiction story and a non fiction piece that discusses some of the real science referenced in the story.

In the meantime, I’ll continue thinking about how we can use fungus to normalize death and decomposition.

Gramma Bot: Twitter Bot Final

Featured Posts, Twitter Bot Workshop

An anti-harassment twitter bot that questions the offender about their decision to use inflammatory language.

I was inspired by a teacher friend of mine who uses a socratic technique when his students call each other names. “Why did you call her that? What does that mean?” I wanted to use a bot to initiate conversation (with the bot saying something like, “Why did you feel like using this word?”) in order to make people reflect on the language they use and how it affects others.

One of the bots that inspired this project was Kevin Munger’s anti-harassment bot geared towards racists. The constraints placed on the bot were smart. It was only looking for the n-word, only when combined with an @ reply, and it checked the user’s timeline for prior offenses. Other important measures were taken that wouldn’t be feasible for my project–manually inspecting the profiles, and manually checking to make sure the two users in the interaction aren’t friends. Furthermore, in order for Munger to run his experiment, he had to hide that fact that he was using bots. I had decided that I was going to be transparent about my bot’s bot status.

I also read studies that had already done textual analysis of slurs used on twitter, like this one. Reading studies like this were important, because I realized that there was no magical Markov chain that was going to help me identify harassment on twitter without false negatives/positives. Even human experts can’t agree on what constitutes harassment. Here’s a quote from the study I linked to: “In manually coding, analysts had considerable difficulty achieving inter-annotator agreement (the extent to which both annotators agreed on the meaning of the same tweet).”

Finally, I wanted to see what other sorts of anti-harassment bots are out there. Even though there are quite a few, I had to restrict myself to bots whose code is written in javascript. This source code really jump-started my project. This is a simple bot created for a hackathon. It takes a whole slew of offensive terms and gives them different weights (for example, both “cunt” and the n-word are given weights of 3, while “fat” and “shit” are given weights of 1). If a user tweets something with a weight greater than 3, then the bot tweets out the user name and says “this comment has been marked as offensive and has been recorded.”

There are some glaring problems with this bot. If I tweet out, “I’m so fucking mad, someone just called me a cunt,” then I would get flagged for using a combination of the words “fucking” and “cunt”. That’s what you get with contextless word counters. They can’t tell the difference between a complaint about getting harassed and actual harassment (like, “You’re a fucking cunt.”) Further more, I didn’t want to call out users on a tweet-by-tweet basis. I wanted to track their behavior over time. A person could have a hundred reasons for using the word “bitch” in a tweet. But it’s definitely fair to call them out if they’ve tweeted the word “bitch” a hundred different times, no matter the reason.

The Ideal Bot:
I’m calling my bot “GrammaBot” because I want it to be satirical, rather than preachy. GrammaBot will track the words “bitch” and “cunt” only, because I want it to be relatively limited in scope. It will also only look at users in the United States because people in the UK seem to have a very different relationship with the word “cunt”. If a single user says either of these words more than 4 times, the bot will mention them in a tweet and say some variation of, “You’ve said the word ‘cunt’ 5 times since [date]. GrammaBot is wondering why you keep saying that word!”

Here is the source code for my bot:

So far I’ve only tested my bot in the console log (so I don’t get immediately blocked by Twitter). This is a video of what it looks like when I run my code. Because I’m using a personal account I’m tweeting the term “blahblahblahblah” instead of “cunt” to test that everything works:

Right now I’m only tracking “cunt” instead of “bitch” and “cunt”, which I actually think is good because it’s limiting the amount of data that’s coming in. Unfortunately I haven’t been able to figure out how to simultaneously filter by keyword and location (the twitter api doesn’t allow you to use both parameters at once). The good news is that by not limiting by location, I’m not missing the offenders who don’t have locations associated with their accounts. I’m also limiting the search to non-retweets. I only want the bot to identify OC (Original Content (or, for the more puerile among us, Original “Cuntent”)). You can follow @Gramma_Bot to see the offenders’ messages and Gramma’s reponses!

UPDATE: On the same day Gramma Bot launched (March 25, 2017), the application’s writing privileges were revoked. 

Before Gramma got shut down, a few funny things happened:

  1. The bot flagged itself as an offender so kept calling itself out for using the word cunt. This was a silly thing for me to forget to account for in the code, but it does play into the narrative of “LOOK AT WHAT GRAMMA BOT HAS BECOME”

2. An offender was amused/baited by the bot, responding with, “I work on those numbers, you cunt”

3. A crazy lady (whose account has just been suspended) who has devoted her twitter career to harassing the doctors and nurses who supposedly botched her breast augmentation surgery assumed that it was the nurses who created Gramma Bot to “bully” her.

4. Gramma Bot retweeted quite a few butt photos from “” so I manually got rid of those.

I’m now deciding whether to appeal twitter’s restriction and then neuter my bot so it follows twitter’s automation rules, create Gramma_Bot2 and inevitably get that account suspended, or simply let things be.

RIP Gramma Bot.