Online Correspondance / EU Digital Futures by Polis 180

DATA IS EVERYWHERE 

A collaboration with postdoctoral researcher Martin Skrodzik and artist Tiz Creel.

Data is everywhere, but what does it mean anyway? 

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Of all your internet lifetime, how much could potentially reveal personal or sensitive information? 

How much could data reveal your location, recurrent places, employment details?

What about the information that is not implicitly there? 

All your uploads, tags, conversations, all the statements that perhaps are not true to you anymore, or all the times you acted out of character in social media. 

Data shared privately, publicly.

Data that others shared.

RANDOM QUESTIONS:THE TEST

PART 1: RANDOM QUESTIONS 

An experiment inspired by the personality quiz popular in the 2000s.

 

The experiments intention is to infer personal biometric data based on seemingly random questions. Four questions formulated and design to enable us to mine information without the participants knowing. 

 

We chose to guess the participant’s age.

People

Responded 

80

We recived 82 resposnes witch 2 of them where duplicates

79

Responded 

The participants respoded to more than 5 questions. 1 participant responded no questions.

questons

most

15

Responded 

questons

all

The Chinese zodiac sign reduces the possible years the participants were born into seven options.

1. What is your Chinese zodiac sing? (69 responses)

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Different generations have used various social media throughout their lifetime

We could attribute many of them to a particular generation. Additionally, there are also age groups when it comes to current social media usage.

2. What social media did/do you use? (79 responses)

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Who owns your data?

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3. What was one of your first favourite music albums? (73 responses)

Usually, people will choose whatever is popular while young, probably early teenage years.

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The Chinese zodiac sign reduces the possible years the participants were born into seven options.

4. In what year did you have your first kiss?  (70 responses)

Final Responses 

THE MODEL

An experiment inspired by the personality quiz popular in the 2000s.

 

The experiments intention is to infer personal biometric data based on seemingly random questions. Four questions formulated and design to enable us to mine information without the participants knowing. 

 

We chose to guess the participant’s age.

We will infer the age of the participants with only two factors: the year of the first kiss and the Chinese zodiac sign. 

The information that we know is 

- Most people have their first kiss between 12 and 15 years old according to multiple studies. 

- The Chinese zodiac signs are repeated every 12 years, giving us leverage to expand the average of the first kiss to bettween 10 and 21 years old. 

1. We created a model that calculates the first kiss age on all seven possible years according to the given Chinese zodiac sign. 

 

2. The model selects a year in which the age of the first kiss is between 10 - 21 years old. 

 

The formula predicts the age of the participants with 90% accuracy. 

ERRORS

 

The 10% failure is in all cases people who had their first kiss outside the selected gap between 10 - 21 years old, therefore the model is unable to compute the age. 

 

Another potential discrepancy: the chinese zodiac sign runs form February 1, 2003 – January 21, 2004 witch can create problems with the wetern understanding of new year. However, it seems to not effect in our sample. 

 

Every prediction with one year difference is computed as a valid response. There is a leverage of one year relative to when the test was responded (in this sample is the 30th of June) and the date and moth the participant was born in. An interesting outcome of this discrepancy is that we can know if the participant has or has not yet celebrated its birthday this year.  

“The questions. What was your first favourite album?” And,  “what social media do/did you use?” Are meant to be used as confirmation bias and inspect the responses that the model was unable to compute. 

Disclaimer 80 participants 53 shared the essential data to participate in the age prediction. 

The model was done with the help of excel wizard Maria Skibinska 

PART 2: RANDOM DATA QUESITONS 

As part of our investigation, we wanted to see weather the participants stand regarding their data.
 

5. Do you have something to hide in your data?
(79 responses)

6. Would you agree to have all your data publicly accessible?
(79 responses)

7. Did you change your answer to question 5 after answering question 6?(79 responses)
 

8. Write a text of up to 500 characters on the statement "Data is everywhere" (50 responses)

PART 3: "DEMOGRAPHICS"

9. If you want to take a chance in winning a 50$* Amazon gift card for answering this survey, enter your email address here.
(20 responses)

Disclamier  : * As for the answer to question 9, please note that we are not actually giving away a 50$ amazon gift card. We just wanted to see whether people would enter their email addresses. The chances are exactly 0% for winning a gift card.

We wanted to see how many people would give away their email after the rather suspicious questions on data and the clear disclaimer at the end of the survey that states that there is no gift card and the question is just a test. 

 

Of 83 participants:

  • 12 People who did give away their email, which naturally implies, that they did not read the disclaimer.

  • 6 People gave fake emails, we could assume that they read the disclaimer. As we have one email that reads: hahaIreadthedisclaimer@gmail.com

  • 65 People who did not give away their email, can just hope that they did read the disclaimer.

10. For demographics, would you please tell us your age? (74 responses)

We asked the age for “demographic purposes”, but actually, for us to confirm our predictions and test our model accuracy.

Conclusion 

 

We want you to think, how easy it would be to extract sensitive personal information from your public profiles (including posts, media, descriptions).

How many online tests, quizzes, questionaries have you filled out throughout the years? 

 

How many terms and conditions have you read? 

How many conditions have you ageed to? 

How many companies have your data? 

As internet users, how much do you really really really care about your data? 

 

The goal of this experiment was to see how easy it is to infer data from seemingly innocent questions, information that we regularly share on social media.

 

Make your conclusions.