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What do Data Visualisations want?

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  • 1. What do data visualisations want? Farida VisInformation 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 imageNew York TimesWashington PostThe TimesThe IndependentHebron Massacre (1994)Beit Lid Bombings (1995)Assassination Rabin (1995) 500 articles (10 days)(visual) content analysisFrame analysisDiscourse analysis
  • 4. Pictures do not speak for themselvesThey need wordsPower of the caption (preferred reading, anchor)
  • 5. Hurricane Katrina: pictures, captions,online responses
  • 6. Fitna: the video battle
  • 7. Fitna, the video battle: how YouTubeenables the young to perform theirreligious 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. MethodsThecorpus of 1413 videos. Throughpurpose builtAPItool: country, age, gender, video ID, URL, author name,title etc. (tool: http://lexiurl.wlv.ac.uk/)Content analysisNetwork analysis(subscriptions, friends, comments)(made with off the shelf fur ball generator)Genre analysisSurvey
  • 9. In the presentation of our results, we have anonymized thechannel names. Although YouTube videos can be consideredsemi-public data, the kind of network analysis presentedhere presents combinations and relations that posters maynot have wished to be easily and publicly available. A fullyannotated 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)EthicsSuggested connectedness by the deviceTemporality – data over time difficult to highlightWorkings of the algorithm obscureMeaning derived from quantitative and qualitative methods
  • 10. READINGTHE RIOTSON TWITTER Rob Procter (University of Manchester) Farida Vis (University of Leicester)Alexander Voss (University of St Andrews) [Funded by JISC] #readingtheriots
  • 11. Guardian Interactive Team (Alastair Dant)http://www.guardian.co.uk/uk/interactive/2011/dec/07/london-riots-twitter
  • 12. Data visualisations cannot speak for themselvesThey need words, storiesHow are they made? What are their limits?Twitter: no access to the data (not allowed)
  • 13. If (I am!) interested in meaning, datavisualisation often only the startWay to get back in to the data – dig deeperFind meaning, context, mess
  • 14. Identify the useful data(little data) in the big data
  • 15. Most mentioned riot accounts1. paullewis 30031 mentions3. piersmorgan 20412 mentions4. bbcnews 18836 mentions5. itv_news 15177 mentions6. bbcbreaking 13476 mentions7. guardian 11513 mentions8. lawcol888 9290 mentions9. simonpegg 9240 mentions10. gmpolice 8904 mentions
  • 16. The problem with mentionsThe problem with visualising from textual data?What do they tell you? Frequency important?Who mentions? How do they mention?Context. Context. Context.
  • 17. 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?)
  • 18. Visualising visual dataTwit-picing the riots: what were people sharing,looking at?Riots and crisis situations: very visualData reduction can be a problemShow all the data – how?
  • 19. For analysis of data (text + images) Data visualisation+ in depth analysis (& critique) = next level
  • 20. ‘What do data visualisations want?’ post:http://researchingsocialmedia.org/2012/05/24/what-do-data-visualisations-want/