A couple months ago the Special Libraries Association hosted a webinar: Using Analytics to Understand Social Media Activity. The abstract for the event mentioned the lack of research into the success of social media use by libraries, and promised presenters would “suggest better strategies for user engagement,” as well as “tools and methods that can be used to conduct social media analytics.” Unfortunately I was unable to attend the webinar in real-time, but the SLA has made the webinar’s audio, a transcript, and the PowerPoint presentation available to view on its website.

http://www.google.co.uk/intl/en/analytics/features/mobile.html

Michelle Chen, an assistant professor at the School of Information at San Jose State University presented the webinar. Dr. Chen discussed two research projects. One was designed to facilitate an understanding of user behavior and its implications in online environments. Another used information visualization to enhance knowledge discovery.

Research Project #1:Twitter Sentiment Analysis for Understanding Citizens’ Trust in Government

• Collected 1 million tweets from users reacting to official city, mayor, and police department accounts
• Conducted sentiment analysis (also called “opinion mining”) to uncover citizen attitudes towards government

Sentiment Analysis, Michelle Chen. 

Research Project #2: Quantitative Analytics for Library User Engagement Strategies through Social Media: Pinterest and Twitter

•  Studied 10 libraries using Pinterest and Twitter and categorized engagement in 4 categories: Literature exhibits, Engaging topic, Community building and Library showcasing.
• Developed metrics for analyzing Pinterest (followers, re-pinning, liking, etc.) and Twitter (topic modeling- looked for patterns in topics) 

Some Recommended Tools

• Many Eyes — a data visualization tool that is free and requires no programming or technical expertise (see a free video tutorial here)
• KDNuggets — data mining techniques website
• Splunk, an operational intelligence platform: “It enables the curious to look closely at what others ignore—machine data—and find what others never see: insights that can help make your company more productive, profitable, competitive and secure.”
• R — a free, open-source statistical programming language that allows users to interpret, interact with and visualize data quickly and easily.

Conclusions about Ways Social Media Analytics Can Be Used: 

• To understand library users’ attitudes and to identify trends
• To manage online identity/reputation of a library
• To predict user behavior / needs
• To tailor social media campaigns and effectively target users
• To identify “primary influencers” in social networks and target them