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.

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. 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

4. 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)

5. 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?

6. Conversations around
Matthew Darbyshire’s exhibition
Sources:
• Twitter
• Facebook
• Instagram
• Blogs /
Articles

7. 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)

8. Conversations around
Matthew Darbyshire’s exhibition
Content:
• Photos & comments
• Marketing
• Reviews sharing
• Events reporting
• Likes/favourites

9. Word Cloud based on Tweets

10. Word Cloud based on Tweets

11. 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

12. Sample: Social Network Analysis
Retweets Mentions

13. Literature Festival event

14. 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/

15. La Magnetica, #AskACurator through Social Network Analysis, 2014.
http://www.lamagnetica.com/en/askacurator-through-social-network-
analysis/
Existing research:
SNA

16. 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

17. 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.

18. 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?