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Methodological
Challenges
IR14 Pre-Conference Workshop Denver,CO
dp.woodford@qut.edu.au
@dpwoodford
Wednesday, 23 October 13
PLATFORMS
• Twitter: Open API, Limited on Volume, Real-Time unless
paying for API; good data on user profiles.
• Facebook: Multiple levels of access; public pages easily
accessible, historical data easy to obtain (unless deleted),
but less precise (e.g. likes not timestamped unless page
owner).
• Weibo? Vkontake? LinkedIn?
• Cost:Benefit analysis for developing infrastructure,

Wednesday, 23 October 13
TWITTER
• Streaming API is limited to ~1% of total tweets per second
& Firehose access is expensive.
• Large data sets are not easily malleable, or visually
analyzed (e.g. with Tableau):
– Our database of Twitter users is ~3.7TB, and growing.
– A weeks worth of selected TV data (current US shows) in JSON
format is 750MB, and 600MB in TSV (selected fields). And
millions of rows.

• Analyzing large data sets is slow, if it’s even possible =>
“Usable Data”

Wednesday, 23 October 13
WHAT DOES 1% LOOK LIKE DURING
SCANDAL?

Wednesday, 23 October 13
RANDOM SAMPLING VS FULL SAMPLE
ANALYSIS

Source:	
  Tony	
  Hirst	
  (Open	
  University	
  UK)

Wednesday, 23 October 13
FULL SAMPLE, REPEATED CAPTURE

Source:	
  Bruns	
  /	
  Woodford	
  [Mapping	
  Online	
  Publics]

Wednesday, 23 October 13
ANALYSIS TOOLS: DATA ANALYTICS
• Historically @ CCI / Mapping Online Publics: .csv files,
Gawk Scripts, Excel.
• Ideally: Custom Tools, R, Large Sample analysis.
• Reality: We’ve compromised on Tableau. A great option for
easy data analysis, and reasonably large samples:
– But still, large flat files => memory leaks (e.g. Accession charts
for Barack Obama).
– Currently investigating database solutions, with mySQL as a
stop-gap.

Wednesday, 23 October 13
ANALYSIS TOOLS: CONTENT ANALYSIS

Source:	
  Woodford	
  /	
  Prowd	
  [Fan	
  Cultures	
  and	
  Hatred	
  in	
  Big	
  Brother	
  15:	
  Race	
  Rows,	
  EliMsm	
  &	
  SporMng	
  Tribalism	
  -­‐-­‐	
  Forthcoming]

• Lots of advanced tools, but sometimes they overcomplicate things; simple visuals are good for
communicating to your audience..

Wednesday, 23 October 13
ANALYSIS TOOLS: WORDLE SOMETIMES
WORKS

Source:	
  Woodford	
  /	
  Prowd	
  [Fan	
  Cultures	
  and	
  Hatred	
  in	
  Big	
  Brother	
  15:	
  Race	
  Rows,	
  EliMsm	
  &	
  SporMng	
  Tribalism	
  -­‐-­‐	
  Forthcoming]

Wednesday, 23 October 13
ANALYSIS TOOLS: WORDLE SOMETIMES
WORKS

Source:	
  Woodford	
  /	
  Prowd	
  [Fan	
  Cultures	
  and	
  Hatred	
  in	
  Big	
  Brother	
  15:	
  Race	
  Rows,	
  EliMsm	
  &	
  SporMng	
  Tribalism	
  -­‐-­‐	
  Forthcoming]

Wednesday, 23 October 13
WHO’S THE AUDIENCE?
• Who you want to connect with is important in choosing
methods and presentations.
• My commentary / analysis on Big Brother 15 (US) across
my blog, MOP and SocialMedia@QUT was read many
more times than ANY academic article I’ve ever written.
• And was picked up by industry..
• Who should we be writing for?

Wednesday, 23 October 13
ACKNOWLEDGEMENTS
• ARC Centre for Excellence in Creative Industries and
Innovation (CCI) - http://www.cci.edu.au & http://
www.mappingonlinepublics.net
• Social Media Research Group -- http://
socialmedia.qut.edu.au
• Queensland University of Technology

Wednesday, 23 October 13

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IR14 Pre-Conference Workshop Lightning Talk: Social Media Methods & Ethics

  • 1. Methodological Challenges IR14 Pre-Conference Workshop Denver,CO dp.woodford@qut.edu.au @dpwoodford Wednesday, 23 October 13
  • 2. PLATFORMS • Twitter: Open API, Limited on Volume, Real-Time unless paying for API; good data on user profiles. • Facebook: Multiple levels of access; public pages easily accessible, historical data easy to obtain (unless deleted), but less precise (e.g. likes not timestamped unless page owner). • Weibo? Vkontake? LinkedIn? • Cost:Benefit analysis for developing infrastructure, Wednesday, 23 October 13
  • 3. TWITTER • Streaming API is limited to ~1% of total tweets per second & Firehose access is expensive. • Large data sets are not easily malleable, or visually analyzed (e.g. with Tableau): – Our database of Twitter users is ~3.7TB, and growing. – A weeks worth of selected TV data (current US shows) in JSON format is 750MB, and 600MB in TSV (selected fields). And millions of rows. • Analyzing large data sets is slow, if it’s even possible => “Usable Data” Wednesday, 23 October 13
  • 4. WHAT DOES 1% LOOK LIKE DURING SCANDAL? Wednesday, 23 October 13
  • 5. RANDOM SAMPLING VS FULL SAMPLE ANALYSIS Source:  Tony  Hirst  (Open  University  UK) Wednesday, 23 October 13
  • 6. FULL SAMPLE, REPEATED CAPTURE Source:  Bruns  /  Woodford  [Mapping  Online  Publics] Wednesday, 23 October 13
  • 7. ANALYSIS TOOLS: DATA ANALYTICS • Historically @ CCI / Mapping Online Publics: .csv files, Gawk Scripts, Excel. • Ideally: Custom Tools, R, Large Sample analysis. • Reality: We’ve compromised on Tableau. A great option for easy data analysis, and reasonably large samples: – But still, large flat files => memory leaks (e.g. Accession charts for Barack Obama). – Currently investigating database solutions, with mySQL as a stop-gap. Wednesday, 23 October 13
  • 8. ANALYSIS TOOLS: CONTENT ANALYSIS Source:  Woodford  /  Prowd  [Fan  Cultures  and  Hatred  in  Big  Brother  15:  Race  Rows,  EliMsm  &  SporMng  Tribalism  -­‐-­‐  Forthcoming] • Lots of advanced tools, but sometimes they overcomplicate things; simple visuals are good for communicating to your audience.. Wednesday, 23 October 13
  • 9. ANALYSIS TOOLS: WORDLE SOMETIMES WORKS Source:  Woodford  /  Prowd  [Fan  Cultures  and  Hatred  in  Big  Brother  15:  Race  Rows,  EliMsm  &  SporMng  Tribalism  -­‐-­‐  Forthcoming] Wednesday, 23 October 13
  • 10. ANALYSIS TOOLS: WORDLE SOMETIMES WORKS Source:  Woodford  /  Prowd  [Fan  Cultures  and  Hatred  in  Big  Brother  15:  Race  Rows,  EliMsm  &  SporMng  Tribalism  -­‐-­‐  Forthcoming] Wednesday, 23 October 13
  • 11. WHO’S THE AUDIENCE? • Who you want to connect with is important in choosing methods and presentations. • My commentary / analysis on Big Brother 15 (US) across my blog, MOP and SocialMedia@QUT was read many more times than ANY academic article I’ve ever written. • And was picked up by industry.. • Who should we be writing for? Wednesday, 23 October 13
  • 12. ACKNOWLEDGEMENTS • ARC Centre for Excellence in Creative Industries and Innovation (CCI) - http://www.cci.edu.au & http:// www.mappingonlinepublics.net • Social Media Research Group -- http:// socialmedia.qut.edu.au • Queensland University of Technology Wednesday, 23 October 13