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What do Data Visualisations want?


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What do Data Visualisations want?

  1. 1. What do data visualisations want? Farida VisInformation School, University of Sheffield @flygirltwo
  2. 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. 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. 4. Pictures do not speak for themselvesThey need wordsPower of the caption (preferred reading, anchor)
  5. 5. Hurricane Katrina: pictures, captions,online responses
  6. 6. Fitna: the video battle
  7. 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:
  8. 8. MethodsThecorpus of 1413 videos. Throughpurpose builtAPItool: country, age, gender, video ID, URL, author name,title etc. (tool: analysisNetwork analysis(subscriptions, friends, comments)(made with off the shelf fur ball generator)Genre analysisSurvey
  9. 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. 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. 11. Guardian Interactive Team (Alastair Dant)
  12. 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. 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. 14. Identify the useful data(little data) in the big data
  15. 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. 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. 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. 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. 19. For analysis of data (text + images) Data visualisation+ in depth analysis (& critique) = next level
  20. 20. ‘What do data visualisations want?’ post: