Slides from talk at ODI Fridays, 19 May 2017 https://theodi.org/lunchtime-lectures/friday-lunchtime-lecture-open-your-effing-data

1. peter.wells@theodi.org
@peterkwells

2. WARNING
This talk will contain language that
will offend some people, including
my mum.
@peterkwells

3. Open your effing
data
@peterkwells

4. @peterkwells
talk

5. Once upon a time... I saw a
126-page PDF containing
beautiful data.
@peterkwells

6. Bollocks.
(Medium offensiveness. Less
problematic when used to mean
‘nonsense’.)
@peterkwells

7. The PDF contained swear
words.
@peterkwells

8. I turned the swear words
into data.
@peterkwells

9. @peterkwells

10. Sometimes you publish bad
data. People will help you
correct it.
@peterkwells

11. Data needs context.
@peterkwells

12. @peterkwells
Swear word data needs context.

13. @peterkwells
Examples exist to help.

14. @peterkwells
http://tiny.cc/lotsofswears

15. Bollocks.
General swear words and body parts.
Medium language, potentially unacceptable pre-watershed. Not generally offensive but
somewhat vulgar when used to refer to testicles. Less problematic when used to mean
‘nonsense’.Less problematic when used to mean ‘nonsense’.
Not amongst least recognised words.
@peterkwells

16. @peterkwells
Houston, we
are ready to
fucking use
swear word
data.

17. @peterkwells
Getting data
to people who
need it.

18. @peterkwells
But what do people need
swear word data for?

19. @peterkwells
Swearing.

20. @peterkwells
Arse.
Balls.
Knob.
Bastard.

21. @peterkwells
“The main purpose of
swearing is to express
emotions, especially anger
and frustration.”

22. @peterkwells
I suspect some open and
data people swear quite a
lot out of frustration...

23. @peterkwells
Broadcasters use
swear words to
do their job.

24. @peterkwells
How did society decide which
words should be in this
offensive word list?

25. @peterkwells
The regulator did user research.

26. @peterkwells
Democracy.

27. @peterkwells
“Ofcom’s principal duty is to further the
interests of citizens and of consumers,
where appropriate by promoting
competition. Meeting this duty is at the
heart of everything we do.”

28. @peterkwells
Regulators use
swear words to
do their job
too… ENTER PIN TO UNLOCK

29. @peterkwells
Some people
might use swear
word data for
data-driven
censorship.

30. @peterkwells
Censorship by
data can go
wrong. ENTER PIN TO UNLOCK

31. @peterkwells
And not just on
TV.
ENTER PIN TO UNLOCK

32. @peterkwells
Remember context?

33. @peterkwells
Offensive on tv and radio.

34. @peterkwells

35. @peterkwells
Suppressed
car registration
plates.

36. @peterkwells
Suppressed
car registration
plates.

37. @peterkwells
Political car
registration
plates.

38. @peterkwells
How do we decide what should
be suppressed on car
registration plates?

39. @peterkwells
Unparliamentary
language

40. @peterkwells
Unparliamentary
language in Australia.

41. @peterkwells
http://tiny.cc/parliamentaryswears

42. @peterkwells
How do we decide what is
unparliamentary language?

43. @peterkwells
Hate speech from
across the world.

44. @peterkwells
How do we decide what hate
speech is?

45. @peterkwells
Context is a very complex six
letter word.

46. @peterkwells
Swearing philosophers know
this.

47. @peterkwells
"Oyston" is an
offensive word to
my community of
Blackpool fans.

48. @peterkwells
A car registration
plate that was
offensive to my
community.

49. @peterkwells
Offensiveness changes over
time and can be local to a
community.

50. @peterkwells
“the frothy mixture of
lube and fecal matter
that is sometimes the
byproduct of anal sex”

51. @peterkwells
People game
systems.

52. @peterkwells
People adapt.
So it goes.

53. @peterkwells
People is a more complex six
letter word than context.

54. @peterkwells
Machine learning to the rescue?

55. @peterkwells
An example of a machine that
learns

56. @peterkwells
How will machines decide what
is offensive?

57. @peterkwells
There’s a patent.

58. @peterkwells
Context.

59. @peterkwells
Maybe people can help the
machines?

60. @peterkwells
Decentralised user research?

61. @peterkwells
Task: go to a football ground in Gdansk,
Poland. Play this video to people near you.
Observe their attitude to you, and each
other, over the following seven days before
categorising the offensiveness of the video

62. @peterkwells
How will the organisations who
decide to use the machines
decide what is offensive?

63. @peterkwells
Rabbit hole

64. Bollocks.
(Medium offensiveness. Less problematic
when used to mean ‘nonsense’.)
@peterkwells

65. @peterkwells
Complex problems will only be tackled by
starting small....

66. @peterkwells
Swear words are data.
That data can be open.
That data will meet some needs.

67. @peterkwells
Context is important and
complex.
People are far more important
and far more complex.

68. @peterkwells
People, organisations,
communities and societies make
decisions about what is
offensive.

69. @peterkwells
Technology changes.
People adapt.
So it goes.

70. @peterkwells
p.s. start small, open
your effing data

71. @peterkwells
SORRY MUM.

72. WARNING
This talk contained language that
will have offended some people.
@peterkwells

73. Sources
Slide 4: fair use http://metro.co.uk/2016/10/02/swearing-ranked-from-mild-to-strongest-6165629/
Slide 9: CC0 https://pixabay.com/p-254028/?no_redirect
Slide 12, 25, 33: Ipsos Moris, OGL https://www.ofcom.org.uk/__data/assets/pdf_file/0022/91624/OfcomOffensiveLanguage.pdf
Slide 13: OpenPrescribing.net, EBM DataLab, University of Oxford, 2017 https://openprescribing.net/caution/
Slide 14: made using https://octopub.io/ by ODILabs
Slide 16: public domain https://www.nasa.gov/audience/foreducators/rocketry/imagegallery/rpd_AtlasV.jpg.html#.WRiOHlPytE5
Slide 17: CC-BY-SA 3.0: https://en.wikipedia.org/wiki/File:Green_tick.png
Slide 21: Timothy Jay and Kristin Janschewitz:
https://www.mcla.edu/Assets/MCLA-Files/Academics/Undergraduate/Psychology/Pragmaticsofswearing.pdf
Slide 23, 28: Roger Mellie, copyright Viz
Slide 26: Nick Youngson, http://nyphotographic.com/ CC-BY-SA-3.0NY http://thebluediamondgallery.com/d/democracy.html
Slide 27: what is Ofcom, OGL: https://www.ofcom.org.uk/about-ofcom/what-is-ofcom
Slide 29: Mary Whitehouse, image via Wikipedia, fair use https://en.wikipedia.org/wiki/Mary_Whitehouse
Slide 30: image copyright Disney, fair use
Slide 31: McVities Ginger Nuts, fair use
Slide 34: Jaws, copyright Universal Pictures, fair use
Slide 35, 36, 37: list sourced from DVLA by FOI, should be open data:
https://www.whatdotheyknow.com/request/numberplate_blacklist#incoming-548093
Slide 36: find out more about the IRA and the UDA by doing the dog : https://www.youtube.com/watch?v=uWpQ54f8cA4
Slide 40: via Tim Sherratt https://github.com/wragge/hansard-language
Slide 41: made using https://octopub.io/ by ODILabs
Slide 43: find out more: https://www.hatebase.org/recent_updates
@peterkwells

74. More sources
Slide 46: Rebecca Roache in Aeon magazine, fair use:
https://aeon.co/essays/where-does-swearing-get-its-power-and-how-should-we-use-it
Slide 47: support the campaign by Blackpool fans to put football first: http://www.blackpoolsupporterstrust.com/Site/Default.aspx
Slide 48: via Worzelo on twitter, fair use: https://twitter.com/pnefc1973/status/561626482892877825
Slide 49: Gage Skidmore CC-BY-SA3.0
https://en.wikipedia.org/wiki/Rick_Santorum#/media/File:Rick_Santorum_by_Gage_Skidmore_8.jpg
Slide 52, 69: read more Kurt Vonnegut
Slide 52: CC0 https://pixabay.com/p-431230/?no_redirect
Slide 57, 58: Licence somewhere in here https://www.google.ch/intl/en/policies/terms/regional.html I’m saying “fair use” from
https://www.google.ch/patents/US20150309987
Slide 60: Annalee Newitz via Ars Technica, fair use https://arstechnica.co.uk/features/2017/04/the-secret-lives-of-google-raters/
@peterkwells