1. Data visualizations do not speak for themselves and need words and context to provide meaning. 2. While data visualizations are useful for identifying patterns, they often only scratch the surface and deeper qualitative analysis is needed to understand the full context and meaning. 3. When analyzing social media data, data visualizations are limited because they reduce large amounts of data and obscure important details; combining visualizations with in-depth qualitative analysis provides a more complete understanding.

1. What do data visualisations want?
Farida Vis
Information School, University of Sheffield
@flygirltwo

2. What do pictures want?
W.J.T. Mitchell (2006) - Pictures:
1. How they are produced?
2. What do they mean?
3. What about the pictures themselves?
What do they want (if they were alive?)

3. PhD: newspaper text and image
New York Times
Washington Post
The Times
The Independent
Hebron Massacre (1994)
Beit Lid Bombings (1995)
Assassination Rabin (1995)
500 articles (10 days)
(visual) content analysis
Frame analysis
Discourse analysis

4. Pictures do not speak for themselves
They need words
Power of the caption (preferred reading, anchor)

5. Hurricane Katrina: pictures, captions,
online responses

6. Fitna: the video battle

7. Fitna, the video battle: how YouTube
enables the young to perform their
religious and public identities
Liesbet van Zoonen (PI)
SabinaMihelj (Co-I)
Farida Vis (RA)
Mike Thelwall (honorary member)
May 2009 – May 2010
Project site: http://bit.ly/Fitnaresearch

8. Methods
Thecorpus of 1413 videos. Throughpurpose builtAPI
tool: country, age, gender, video ID, URL, author name,
title etc. (tool: http://lexiurl.wlv.ac.uk/)
Content analysis
Network analysis(subscriptions, friends, comments)
(made with off the shelf fur ball generator)
Genre analysis
Survey

11. In the presentation of our results, we have anonymized the
channel names. Although YouTube videos can be considered
semi-public data, the kind of network analysis presented
here presents combinations and relations that posters may
not have wished to be easily and publicly available. A fully
annotated version of our analyses is available on request (6).
From: Van Zoonen, L., Vis, F., and Mihelj, S., (2011), ‘YouTube interactions between agonism,
antagonism and dialogue: Video responses to the anti-Islam film Fitna’, New Media & Society, 13(8):
1284-1300.(emphasis mine)
Ethics
Suggested connectedness by the device
Temporality – data over time difficult to highlight
Workings of the algorithm obscure
Meaning derived from quantitative and qualitative methods

12. 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

13. Guardian Interactive Team (Alastair Dant)
http://www.guardian.co.uk/uk/interactive/20
11/dec/07/london-riots-twitter

57. Data visualisations cannot speak for themselves
They need words, stories
How are they made? What are their limits?
Twitter: no access to the data (not allowed)

58. If (I am!) interested in meaning, data
visualisation often only the start
Way to get back in to the data – dig deeper
Find meaning, context, mess

59. Identify the useful data
(little data) in the big data

60. Most mentioned riot accounts
1. paullewis 30031 mentions
3. piersmorgan 20412 mentions
4. bbcnews 18836 mentions
5. itv_news 15177 mentions
6. bbcbreaking 13476 mentions
7. guardian 11513 mentions
8. lawcol888 9290 mentions
9. simonpegg 9240 mentions
10. gmpolice 8904 mentions

61. The problem with mentions
The problem with visualising from textual data?
What do they tell you? Frequency important?
Who mentions? How do they mention?
Context. Context. Context.

62. What do data visualisations want?
1. How they are produced?
2. What do they mean?
3. (What about the data visualisations
themselves?)
What do they want (if they were alive?)

63. Visualising visual data
Twit-picing the riots: what were people sharing,
looking at?
Riots and crisis situations: very visual
Data reduction can be a problem
Show all the data – how?

65. For analysis of data (text + images)
Data visualisation
+ in depth analysis (& critique)
= next level

66. ‘What do data visualisations want?’ post:
http://researchingsocialmedia.org/
2012/05/24/what-do-data-visualisations-
want/