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Our Data Ourselves

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Jennifer Pybus

on 27 June 2018

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Transcript of Our Data Ourselves

Key Research Questions Asked - Focus Groups?
1. How they understood privacy within a digital and datafying landscape?
2.What strategies are used to maintain privacy?
3. Is there apathy in relation to the amount of data that young people are willing to share about themselves online?


Key Points in the Literature
1) Privacy Paradox:
Where intentions and behaviour do not match (Acquisti & Grossklags; Norberg, Horne & Horne; Shklovski et. al., Pasquale)

2) Contextual Integrity--Context Collapse?:
Focuses less so on how data is used but rather who uses it and for what purpose. (Nissenbaum)

3) Datafication:
Role of datafication, taken partly Mayer-Shonberger and Cukier, they relate this to the potential value generated by our data.
Methodology: Interdisciplinary Approach
1. SmartPhones & MobileMiner

2. Focus Groups & Interviews

3. Tumblr Diaries

4. Hackathons
Anxieties
Participant G:
You don't have privacy. If anyone said to me now, that I have complete control over everything that I post online, I would say no I do not.

Participant H:
People don't realize how large their digital footprint’s actually are. I've been looking into this recently, like anonymity online and it is incredibly easy to track down the personal details of someone, pretty much anyone in this room with a simple email address and a name.

3. Privacy is About the Collective
Here privacy is something that is “
attached to other people…so if someone you agree to connect with is open then you can be accessed through them cause it's kind of herd thing, you've all got to do it otherwise, one person is in trouble
.”
Data as Making: Hackathons and Data Literacy
Participant F: Sonification of Data
Most people would prefer this to numbers on a screen or paper, as its a lot more jarring for the non-savvy, as some could say that higher, louder tones are uncomfortable, whereas seeing numbers is relatively meaningless unless you know the context...This could break down the complicatedness for the end user...It would be great if you could listen to a list of apps, to find the tones, to find the one that are potential problems.

Data as Making: Hackathons and Data Literacy
Participant G: Quantification of Social Media Usage.
Maybe in future this could work on a daily basis over months and years rather than just a week. We could see how social media is used more on important days such as during big events. For others it could also be used for statistical analysis along with other social media datasets to create a wider picture of what we do online.

Our Data Ourselves
Emergent Discussion Themes
A) The capacity to control a message being sent about oneself;



B) The degree of one’s understanding of the technological affordances of any given app.
Moving Forward...

How can these hacking practices and tools be taken up so that people who are not technologically literate can participate more meaningfully in these discussions?

What role can the interdisciplinary workshop play in bringing disparate people together to discover, as opposed to only create technological objects that should be available to everyone to unpack processes of datafication?
Our Data Ourselves (Nov. 2013 - Dec. 2014)
Dr. Jennifer Pybus
jennifer.pybus@lcc.arts.ac.uk
@jrpybus
Participant A: Being of kind of this generation and being tech savvy we have some control because we know how to have control, where as I know that my mum doesn't have any idea…We know we can control our privacy and a lot of people do but then a lot of people also go and they are just using the technology but don’t actually understand how it works.
Participant B: 'If you have nothing to hide you have nothing to fear.'
Open Rights Group
December 21, 2016
The landscape for teens: The digital economy needs you!
Starting point:

1. Our cultural practices extend our networked connectivity and make this datafied digital economy possible.

2. How can we consider the unprecedented amount of 'big social data' that young people produce through mediated cultural and communicative practices both on the Internet and now increasingly on mobile devices.
The largest sites where social/cultural data is production by young people are on social media platforms
$321B (Fifth largest company in the world)
Number of FB members: 1.7B
Number Likes a minute: 4M
Number of photos every day: 350M
Number videos watched: 8.1B/day
Amount of time spent watching videos: 100M hours/day
$26B
$35B
AND HOW ARE THEY PRODUCING DATA?
St. Peter's Square during the announcement of Pope Benedict in 2005
St. Peter's Square during the announcement of Pope Francis in 2013
In the US 73% of youth (11-13 )have mobile devices
In the UK 97% of youth (11-13) have mobile devices
Volume
: The amount of of data that can now be processed.
Velocity
: Increased speed by which data can flow in and out of an organization (i.e. think Amazon or Netflixs)
Variety
: The number of sites in which data can now be produced and incorporated.
Note: The relationality of data
Understanding the datafied economy means understanding big data
Digitization vs. Datafication
Digitization

Datafication
Is the about moving content from analogue state to a digital state
Digital content is in binary code 0s & 1s - the language of computers
i.e. changing a record into a CD-rom.
Datafication is about the extraction of data from already existing objects (analogue or digital)
It is about the quantification of material or symbolic/cultural objects or practices to produce new value(s)
About reuse of these existing analogue or digital objects.

Affectiva
Why did this matter to us?
Apps are siloed, have less oversight and can gather more data about their users within opaque 3rd party ecosystems. They are also increasingly young people's gateway to the Internet.
MobileMiner
Examined the
frequency
at which apps regularly harvest data
Location data was taken from
opencellid.org

not
GPS
Data harvested was placed in the
CKAN
(comprehensive knowledge archive network) platform and would be used later by YRSers
We packaged the CKAN data with a standard toolbox so the protoype of what we called "
Big Social Data Commons
" could be worked over in a virtual machine and was freely available to download.
MobileMiner made visible the dynamic ways in which sociality becomes datafied via mobile applications.
MobileMiner Preliminary Results: Not all apps are equal
Don't Tap the White Line:
45 - 55 times in 21 days


Line Keep In:
1760 times in 27 days
Full transcript