This presentation makes an argument for a more central focus on images within social media research. It offers approaches and concrete examples from both 'Big data' and 'small data' perspectives. Presented at the Digital Transformations in the Arts and Humanities: Big Data Workshop, London, June 25 2013.

1. Where do images fit in the era of ‘Big
Data’? - Analysing social media images
from a Big and small data perspective.
Farida Vis, Information School
University of Sheffield
@flygirltwo

2. READING
THE RIOTS
ON TWITTER
Rob Procter (University of Manchester)
Farida Vis (University of Leicester)
Alexander Voss (University of St Andrews)
[Funded by JISC]
#readingtheriots

3. What role did social media play?
2.6 million riot tweets (donated by Twitter)
–
700,000 individual accounts
Initially:
o Role of Rumours
o Did incitement take place? [no –#riotcleanup]
o What is the role of different actors on Twitter?

4. Role of Rumours

5. Guardian Interactive Team (Alastair Dant)
http://www.guardian.co.uk/uk/interactive/20
11/dec/07/london-riots-twitter
Data Journalism Award (sponsored by
Google)

9. Data visualizations:
what are they and what do they want?

13. 400 million tweets/day (March 2013)
40 million Instagram images/day (January 2013)
-> 59% posted to Twitter
-> 98% posted to Facebook
130 million active Instagram users
16 billion images on the service
1 billion likes a day (June 2013)

14. Where do images fit in the era of ‘Big Data’?

15. Big Data – text + number driven
Images: undervalued, underexplored
Not by the users

16. Big Data small data

17. Big Data small data
Quantitative Qualitative

18. Big Data small data
Quantitative Qualitative

19. Big Data small data
Quantitative Qualitative
WHY

20. Context
Context
Context

21. UK riots – 2.6 million tweets Egypt protest – 1FB image

22. READING
THE RIOTS
ON TWITTER
Rob Procter (University of Manchester)
Farida Vis (University of Leicester)
Alexander Voss (University of St Andrews)
[Funded by JISC]
#readingtheriots
No analysis of the images
circulated on Twitter

23. The burning bus

24. Image sharing
2.6 million tweets: 10K unique shortened links - 19K shares
(1)image sharing platform
(2)video sharing platform
(3)social media platform
(4)mainstream media – riot coverage
(5)mainstream media – other
(6)Alternative media
(7)Blogs (included in Technorati top 100)
(8)Blogs – other
(9)Websites – news focused;
(10) Websites – other
(11) Spam
(12) Broken link

25. (1) Police car (burning, attack, and aftermath)
(2) Bus (burning, aftermath, and altered image)
(3) Other Vehicle (burning, attack, and aftermath)
(4) Building (burning, aftermath, before/after shots)
(5) Looting (in the act, aftermath, trophy shots)
(6) Screenshots (TV screens)
(7) Street scenes
(8) Police
(9) Arrests
(10) Image of text (screen grab other than TV screen, sign,
newspaper front page)
(11) riot clean up
(12) unclear
(13) Other
(14) excluded (not about riots, not single still image, broken link,
image removed etc.).

26. Total image shares according to image categories

27. ‘Although the Twitter user chose the viewing position and shared the image
through Yfrog the original image data was created by one of Google’s
‘numerous data collection vehicles’ using their R5 ‘panoramic camera system’’
(Anguelov et al., 2010, pp. 32-33).
Re-use of images originally created by
Google Streetview data collection vehicle

28. Deleted content
http://twitpic.com/62m6nx

30. The burning bus – 57 unique URLs

31. Image wall – ten categories

32. (1) The moment the bus went up in flames
(2) Smoking bus (close up of the bus, air full of thick smoke)
(3) Bus on fire (close up of the bus, engulfed in flames)
(4) Sky News – TV visible in shot
(5) Sky News – screengrab (TV not visible, better quality)
(6) Burning bus, police and crowds (high quality news image)
(7) Bus consumed by fire (poor quality focused screengrab)
(8)‘Call of Duty’ (same as smoking bus, but altered with text)
(9)Aftermath (carcass of the burnt bus in close up)
(10) Other

33. Viewing within the
domestic space
Showing the first screen on the ‘second screen’

34. Journalists joining in

35. Images uploaded on Twitpic
AND Flickr using different
devices – different
audiences/users
NB riots not Instagram-ed

36. Was not there, claimed to be

37. Altered images

38. Tottenham as a war zone
Role of popular culture

39. Journalist as witness

40. This reminds us of the way in which John Berger notes
the significance of the act of photography in terms of
the statement: ‘I have decided that seeing this is worth
recording’ (Berger, 1972, p. 179, emphasis in original).

41. Further work needed on how images were discussed

42. How to present/display the data?

43. Lev Manovich
Direct Visualization

44. Lev Manovich
http://www.manovich.net/

45. Aby Warburg mnemosyne

46. UK riots – 2.6 million tweets Egypt protest – 1FB image

48. Algorithmic visibility:
seeing the image through Edgerank

49. Finding and (re)tracing the image

56. Photographer of
the
images
reproduced in
Boing Boing

57. Photographer of
the
original image

59. Included in the Storyful
guidelines
for social media image
verification
Who is the
photographer?
Image altered?
Sequence of images
around the specific
image
See:
http://blog.storyful.com/2012/0
And more

60. Verifying social media images

61. Where do images fit in the era of ‘Big Data’?

62. Big Data small data
Quantitative Qualitative

63. Big Data small data
Quantitative Qualitative

64. Big Data small data
Quantitative Qualitative
WHY

65. • Vis, F., Faulkner, S., Parry, K., Manyukhina, Y., & Evans, L. (in
press), ‘Twitpic-ing the riots: analysing images shared on Twitter
during the 2011 UK riots’, in Weller, K., Bruns, A., Burgess, J.,
Mahrt, M., and Puschmann, C. (Eds.) Twitter and Society, New
York: Peter Lang.
• Vis, F. (in preparation), ‘From Egypt to Wallstreet: tracing a
Facebook protest image across social media and the Internet’