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Future research directions:
Social Media and Culture Metrics
Kostas Arvanitis & Chiara Zuanni
Using Digital Technology To ...
Culture Metrics – Big Data, Better Value?
• Critical examination of mechanisms for
establishing and using data sets in cap...
Social Media Metrics and
Audience Experience
• Do people talk about their cultural
experiences on social media?
Why/who/ho...
Social Media Metrics and
Audience Experience
• What more/different to the ‘Culture Metrics’
system might that data offer c...
Conversations around
Matthew Darbyshire’s exhibition
Sources:
• Twitter
• Facebook
• Instagram
• Blogs /
Articles
Conversations around
Matthew Darbyshire’s exhibition
Tools and Technical/Methodological Issues:
• Building on the Twitter ...
Conversations around
Matthew Darbyshire’s exhibition
Content:
• Photos & comments
• Marketing
• Reviews sharing
• Events r...
Word Cloud based on Tweets
Word Cloud based on Tweets
Sample Data
Twitter
Period 13th-26th October:
• 33 Tweets (of which 16 are
RTs)
• 23 Users:
– 3 MAG staff
– 10 peers
– 10 ...
Sample: Social Network Analysis
Retweets Mentions
Literature Festival event
Existing research: coding tweets
E. Villaespesa, Diving into the Museum’s Social Media Stream. Analysis of the
Visitor Exp...
La Magnetica, #AskACurator through Social Network Analysis, 2014.
http://www.lamagnetica.com/en/askacurator-through-social...
Interpreting this data
• Understanding the context and
motivation of audiences’ social
media activity
• Value and usefulne...
Data Integration, Curation and
Professionalism
• Digital Media Analyst, Metropolitan Museum of Art:
– Establish and overse...
Social Media data & Culture Metrics
• How can the arts use digital technology, social
media and big data more strategicall...
Future research directions: Culture Metrics and Social Media
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Future research directions: Culture Metrics and Social Media

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Slides from Kostas Arvanitis's and Chiara Zuanni's presentation from the 'Using Digital Technology to Assess Quality in the Arts' event, part of Manchester's Policy Week.

Published in: Data & Analytics
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Future research directions: Culture Metrics and Social Media

  1. 1. Future research directions: Social Media and Culture Metrics Kostas Arvanitis & Chiara Zuanni Using Digital Technology To Assess Quality in the Arts, 3 November 2015
  2. 2. Culture Metrics – Big Data, Better Value? • Critical examination of mechanisms for establishing and using data sets in capturing cultural experiences • Impact of the rhetoric of (big) data on producing preconceptions of validity and value • What the gaps in the data are and how these gaps are accounted for in organisational practice • Data cultures and data-driven decision making
  3. 3. Social Media Metrics and Audience Experience • Do people talk about their cultural experiences on social media? Why/who/how/what? • “An online survey published on Tate’s website in December 2012, which aimed to identify the behaviors of our visitors on mobile devices, showed that 26 percent of respondents had shared their own content (blog posts, personal thoughts, photos, etc.) during or after their visit” (Villaespesa, 2013)
  4. 4. Social Media Metrics and Audience Experience • What more/different to the ‘Culture Metrics’ system might that data offer cultural organisations? • How can organisations go about capturing this data and embedding it into their practices? • What are the organisational challenges of a data rich cultural professional practice?
  5. 5. Conversations around Matthew Darbyshire’s exhibition Sources: • Twitter • Facebook • Instagram • Blogs / Articles
  6. 6. Conversations around Matthew Darbyshire’s exhibition Tools and Technical/Methodological Issues: • Building on the Twitter and Instagram APIs VS third-parties tools • API timeout (Twitter 7 days) • Bookmarking VS printing/downloading content • Privacy and ethical issues • Quantitative VS qualitative data • Sample tools used: – Twitter: TAGs, TAGs Explorer, topsy – Facebook: keyword search on MAG’S account (and referrals from other sites) – Instagram: instagram search; tagsleuth; tagboard (search via hashtags)
  7. 7. Conversations around Matthew Darbyshire’s exhibition Content: • Photos & comments • Marketing • Reviews sharing • Events reporting • Likes/favourites
  8. 8. Word Cloud based on Tweets
  9. 9. Word Cloud based on Tweets
  10. 10. Sample Data Twitter Period 13th-26th October: • 33 Tweets (of which 16 are RTs) • 23 Users: – 3 MAG staff – 10 peers – 10 public • Another 15 RTs • 22 Favourites • 0 replies • Reach: 199.334 impressions Instagram Period 20th Sept – 2nd Nov: • 77 images • 54 users • 1573 likes • 47 comments
  11. 11. Sample: Social Network Analysis Retweets Mentions
  12. 12. Literature Festival event
  13. 13. Existing research: coding tweets E. Villaespesa, Diving into the Museum’s Social Media Stream. Analysis of the Visitor Experience in 140 Characters. In Museums and the Web 2013, N. Proctor & R. Cherry (eds). Silver Spring, MD: Museums and the Web. Published January 31, 2013. Consulted October 29, 2015 October 29, 2015 . http://mw2013.museumsandtheweb.com/paper/diving-into-the-museums-social- media-stream/
  14. 14. La Magnetica, #AskACurator through Social Network Analysis, 2014. http://www.lamagnetica.com/en/askacurator-through-social-network- analysis/ Existing research: SNA
  15. 15. Interpreting this data • Understanding the context and motivation of audiences’ social media activity • Value and usefulness of unprompted/unstructured reactions (as opposed to structured surveys) • Accuracy of data • Representativeness of audiences • Different platforms, different users, different uses? • Methodological and ethical issues on capturing and using social media data
  16. 16. Data Integration, Curation and Professionalism • Digital Media Analyst, Metropolitan Museum of Art: – Establish and oversee an analytics programme to monitor and assess departmental channels, platforms, and programmes (metmuseum.org, email marketing, social media channels, mobile apps, audio guide, interactives, and educational multimedia, both online and in-gallery); – Understand the “story” behind the numbers and prepare materials to share those stories with project teams and senior leadership. – Analyze, conduct user research, and develop timely reports to understand the fluctuations in data and identify trends and opportunities to optimize the Museum’s digital platforms and programmes.
  17. 17. Social Media data & Culture Metrics • How can the arts use digital technology, social media and big data more strategically? • What implications for cultural policy derive from the use of this data? • What one improvement would help?

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