Future research directions: Culture Metrics and Social Media

Future research directions: Social Media and Culture Metrics

Kostas Arvanitis & Chiara Zuanni

Using Digital Technology To Assess Quality in the Arts, 3 November 2015

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

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)

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?

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 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)

Conversations around Matthew Darbyshire’s exhibition

Content:• Photos & comments• Marketing• Reviews sharing• Events reporting• Likes/favourites

Word Cloud based on Tweets

Word Cloud based on Tweets

Sample DataTwitter

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

InstagramPeriod 20th Sept – 2nd Nov:• 77 images• 54 users• 1573 likes• 47 comments

Sample: Social Network Analysis

Retweets Mentions

Literature Festival event

Existing research: coding tweetsE. 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/

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

Existing research: SNA

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

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.

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?