Tag Archives: Facebook
We hope you enjoy this week’s list of articles, posts, show&tell descriptions, and visualizations!
I’m Terrified of My New TV: Why I’m Scared to Turn This Thing On — And You’d Be, Too by Michael Price. Michael, a lawyer at the Brennan Center for Justice at the NYU School of Law, describes his experiences with his new “smart” TV. More sensors means more records being stored somewhere you might not have access to. Especially interesting when your device picks up every word you say:
“But the service comes with a rather ominous warning: ‘Please be aware that if your spoken words include personal or other sensitive information, that information will be among the data captured and transmitted to a third party.’ Got that? Don’t say personal or sensitive stuff in front of the TV.”
Public Perceptions of Privacy and Security in the Post-Snowden Era by Mary Madden. A great report from the Pew Research Internet Project. I don’t want to give away any of the juicy stats so head over and read the executive summary.
This Is What Happens When Scientists Go Surfing by Nate Hoppes. It’s not all privacy talk this week. This is a fun article exploring how new sensors and systems are being used to monitor surfers as they train and practice.
How Private Data is Helping Cities Build Better Bike Routes by Shaun Courtney. We covered the new wave of personal data systems and tools feeding data back into public institutions a bit before. Interesting to hear that more cities are investing in understanding their citizens through the data they’re already collecting.
What Do Metrics Want? How Quantification Prescribes Social Interaction on Facebook by Benjamin Grosser. Ben is most commonly known around the QS community as the man behind the Facebook Demetricator, a tool to strip numbers from the Facebook user interface. In this article, published in Computational Culture, he lays out an interesting argument for how Facebook has created a system in which the users, “reimagine both self and friendship in quantitative terms, and situates them within a graphopticon, a self-induced audit of metricated social performance where the many watch the metrics of the many.”
The Cubicle Gym by Gregory Ferenstein. Gregory was overweight, overworked, and in pain. He started a series of experiments to improve his help, productivity, and wellbeing. I enjoyed his mention of using the Quantified Mind website to track cognition. If you find his experience interesting make sure to read a previous piece where he explains what happened when he replaced coffee with exercise.
Maximizing Sleep with Plotly and Sleep Cycle by Instructables user make_it_or_leave_it. A really nice step by step process and example here of graphing an making sense of Sleep Cycle data.
Toilet Matters by Chris Speed. A super interesting post on what a family was able to learn by having access to data on of all things, the amount of toilet paper left on a roll and when it was being used. Don’t forget to read all the way to end so you can get to gems like this:
“[…]the important note is that the source of this data is not only personal to me, it is also owned by me. We built the toilet roll holder and I own the data. There are very few products or smart phone apps that I can say the same about. Usually I find myself agreeing to all manner of data agreements in order to get the ‘free’ software that is on offer. The toilet roll holder is then my first experience of producing data that I own and that I have the potential to begin to trade with.“
E-Traces by Lesia Trubat. A beautiful and fun project by recently graduated design student, Lesia Trubat. Using adruinos and sensors places on the shoes of dances she was able to create unique visualizations of dance movement. Be sure to watch the video here.
Animated Abstractions of Human Data by James E. Pricer. James is an artist working on exposing self-collected data in new and interesting ways. Click through to see a dozen videos based on different types of data. The image above is a capture from a video based on genotypes derived from a 23anMe dataset.
The Great Wave of Kanagawa by Manuel Lima. Although this is an essay I’m placing it here in the visualization section because of it’s importance for those working on the design and delivery of data visualizations. Manuel uses the Great Wave off Kanagawa as a wonderful metaphor for designing how we visually experience data.
D3 Deconstructor by UC Berkeley VisLab. A really neat tool here for extracting and repurposing the data powering at D3.js based visualization.
Shawn Dimantha is always looking for easier ways to track his health. He uses a variety of self-tracking tools, but a few months ago he became interested in exploring what he could do given his engineering and health IT background. He was inspired by immersion, an MIT-developed email analysis tool, which helped him understand who he was communicating with, and by Wolfram Alpha’s Facebook analysis tool. Focusing on Facebook and the wealth of image-based data in his profile he asked himself if images could be a window into his health. After reading a research paper on the use of images to predict body mass index he decided to see what he could learn my implementing a similar procedure on his own images.
What Did Shawn Do?
I used photos from my Facebook account to track my health, the reason I did this because I wanted to see how a simple heuristic I used for tracking my health daily could be implemented in the online world given the huge amount of photos that are and have been shared on a daily basis. I notice when I gain or lose weight, am stressed or relaxed from my seeing my face in my mirror. I was partly inspired by the self-photo collages presented on YouTube.
How Did He Do it?
I selected photos of my face from my Facebook account, cropped out my face and used some software and manual tagging to measure the ratio of different fiducial points on my face (eye-eye length, and cheek to cheek length) over time to help serve as a proxy for my health.
What Did He Learn
Facial image data needs to be cleaned and carefully selected. Face shapes are unique and need to be treated as such. Data that is not present is often more telling than what is present. Life events effect my weight and should be put into context; however causation is harder to determine than correlation. By being more conscious of my score and I can change my behavior before things get off track.
Right now I’m turning this into a product at Enfluence.io where I’m focused on using it to help with preventive health.
Facebook (my own images)
Python / OpenCV
Slides from Shawn’s talk are available here.
I was curious to see if I was the only one crazy enough to share my health data publicly, so last week I posted two questions as my Facebook status. “Would you track your health on Facebook (weight, calories, sleep, exercise) for all your friends to see?”, followed by “What if it was completely private for only you to see?”
The answers I got surprised me. I didn’t expect 26 people to reply. I didn’t expect such detailed opinions. I didn’t expect the answer to be a resounding, 70% yes.
Another surprise was the range and passion of replies, from “no way!’ to “I would love that!”, and everywhere in between. Here are some of the comments I found most interesting, in no particular order.
“I would keep stuff like basic fitness info on something like facebook. I wouldn’t trust medical info here.”
“Public daily measurement is an interesting way to keep you on your diet/exercise plan/meditation schedule, but most people probably want to share the social/personal significance of the data rather than the data itself. e.g. “Mike lost 2 lbs this week! Now he’s 10% of the way to his goal!” rather than daily weight variation.”
“don’t mind anyone seeing this info …it’s just the job of collecting it”
“On one hand, I don’t think my “friends” care what I weigh, etc., though I don’t mind sharing this info with them. In fact, I’d expect some might find this level of personal disclosure somewhat creepy and odd. Developing flexible privacy and data-sharing controls for both the information sharer and recipient will be important.
On the other hand, I’d like to make this info available to researchers and those developing applications for new forms of health monitoring systems. Facebook seems to have emerged as the current leading platform for social networking. It provides a strong platform for application developers to build tools for new types of interaction and collaboration. So, I hope that my participation on the cutting edge of health information monitoring will lead to beneficial new forms of medical practice.
I think social networking enables a new form of participatory science, which is more than passive observation. It allows for real-time feedback, social reinforcement for participants from trusted sources, and dynamically configurable experiments, which can lead to real-world outcomes.”
“I don’t think I would be comfortable doing this. I don’t trust that anything you put on facebook is completely private. I do like the idea though.”
“For what reason exactly? In the interest of being proactive about my health? Would there be a benefit to allowing people to see this info?”
“the caloric intake measure is hard for me…I’m more of a guestimator with food. I’m not sure I’d like everyone to see my weight on here either. Perhaps good motivation, but still. I’d rather have a smaller community know about that (I’m not really a Biggest Loser reality show kind of person).”
“yes — would love to be able to add categories of things to track
and add and remove permissions easily — would rather share a report
than the data”
“to me it’s a simple layer of accountability, like going to the gym with a buddy versus going by yourself. Visibility=incentive. Imagine how many pushups I would do if I did…like at Cross Country practice – team pushing me, not just me+1, 2
here is where economies of scale, intertwining of cyborg lifestyle and quantity+content of connections have perfect opp to mashup”
“Only if I could lie.”
What did I learn from this exercise? The biggest issues raised were privacy and meaning. People wanted to decide WHO got to see which parts of their data. They also wanted to explore WHY they should track themselves and what benefits they would derive. Two people questioned the logistics of how to track. Almost half of the respondents expressed a general mistrust of Facebook in terms of privacy controls.
What else did I learn? Well, maybe I’m not so crazy after all.
Now it’s time to open this up for the QS community to weigh in… Would YOU track your health on Facebook? Post your comments below.