Guest Lecture Irvine
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This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then provides an overview of social network analysis, including key concepts like nodes, edges, centrality measures, and network metrics. Various applications of social network analysis are presented, such as mapping email networks and events on Twitter. Finally, NodeXL is introduced as a tool for visualizing and analyzing social networks.
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Guest Lecture Irvine
- 1. Analyzing Social Media Networks with NodeXL Derek L. Hansen dlhansen@byu.edu
- 2. Center for the Advanced Study of Communities and Information Human-Computer Interaction Lab
- 3. New Tools & Methods to Analyze Social Experience
- 4. Novel Designs of TMSP Interventions
- 5. What is Social Media? A set of networked technologies that supports social interactions. Social media is about “transforming monologue (one-to-many) into dialog (many-to-many).”1 1 www.webpronews.com/blogtalk/2007/06/29/the-definition-of-social-media
- 7. Types of Social Media Asynchronous Threaded Conversation Email, Google groups, Yahoo Answers, Listservs, Stack Overflow Synchronous Threaded Conversation Instant Messaging, IRC, Skype, Google Hangouts Collaborative Authoring Wikipedia, Wikia, Google Docs Blogs & Podcasts Livejournal, Blogger, Twitter, Vlogs, podcasts, photo blogs Social Sharing YouTube, Flickr, Instagram, Pinterest, Last.Fm, Delicious, Reddit, Snapchat Social Networking Facebook, LinkedIn, eHarmony, Ning, Ravelry Online Markets & Production eBay, Amazon, craigslist, Kiva, Thraedless, TopCoder, ePinions, Yelp Idea Generation IdeaConnection, IdeaScale Virtual Worlds Webkinz, World of Warcraft, Club Penguin, Second Life Mobile-based Services Foursquare, MapMyRun, Geocaching
- 8. Patterns are left behind
- 11. • Central tenet – Social structure emerges from – the aggregate of relationships (ties) – among members of a population • Phenomena of interest – Emergence of cliques and clusters – from patterns of relationships – Centrality (core), periphery (isolates), – betweenness • Methods – Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7- 16 Social Network Theory http://en.wikipedia.org/wiki/Social_network
- 12. SNA 101 • Node – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge – Relationship connecting nodes; can be directional • Cohesive Sub-Group – Well-connected group; clique; cluster • Key Metrics – Centrality (group or individual measure) • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • Measure at the individual node or group level – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles – Peripheral – below average centrality – Central connector – above average centrality – Broker – above average betweenness E D F A CB H G I C D E A B D E
- 13. Social Network Analysis A systematic method for understanding relationships between entities. Node-Specific Metrics • Betweenness Centrality • Degree Centrality • Eigenvector Centrality • Closeness Centrality Network-Specific Metrics • Components • Density
- 18. Mapping Corporate Email Communication Between Research Groups
- 19. Mapping Events with Twitter EventGraphs
- 20. Who is Talking about Thimerosal on Twitter?
- 21. #occupywallstreet 15 November 2011 #teaparty 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
- 22. 6 kinds of Twitter social media networks
- 23. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network
- 24. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts and “Answer People” Discussion starters, Topic setters Discussion people, Topic setters
- 26. Surgery Videos on YouTube
- 27. Finding Theorists in Lostpedia
- 29. Social Media Research Foundation People Disciplines Institutions University Faculty Computer Science Information Technology University of Maryland Brigham Young University Students HCI, CSCW Oxford Internet Institute Industry Machine Learning Stanford University Independent Information Visualization Microsoft Research Researchers UI/UX Illinois Institute of Technology Developers Social Science/Sociology Connected Action Network Analysis Cornell Collective Action Morningside Analytics
- 33. Data Sources Code it Yourself Use Free 3rd Party Tools APIs Scrapers Software Libraries Use Corporate Tools
Editor's Notes
- Introduction (family picture, Russia picture, BYU, Umich and iSchools, Maryland - 4 years, HCIL, CASCI, IGERT)Talk about iSchools
- I’m interested in findings ways of leveraging these social technologies to promote the public good.See special issue of IEEE Computer, Nov. 2010 focused on Technology-Mediated Social Participation.
- Much of my work in this area focuses on the analysis of online social networks. Our goal is to democratize the analysis of social network analysis through the creation of new tools, such as NodeXL, and new methods and visualization techniques that help make sense of the mass of data created everyday by social sites.
- My work in this area focuses on designing novel socio-technical solutions to practical TMSP problems. For example, I’ve partnered with FamilySearch Indexing to assess alternate crowdsourcing quality control mechanisms, and developed the “veiled viral marketing” approach to disseminate information on stigmatized illnesses via social networking sites. My current work focuses largely on social games with a purpose – such as Floracaching, which helps collect useful scientific data on plant phenology via a location-based game in the form of a mobile app; Odd Leaf Out, which helps identify errors in plant identification datasets; and Alternate Reality Games which help teach computational thinking skills to youth who help fictional characters solve problems to advance a fictional narrative.
- Thanks to Marc Smith for the slideOne key characteristic of technology-mediated communication is that it can (and typically does) capture detailed data on social interactions.Just like footprints left on the sand tell a story about walking a dog on the beach, our digital footprints tell stories about our online behaviors and interactions.The mass of data created by social media has the potential to usher in a golden age of social science and data-driven decision making.However, to put this social data to good use by researchers, as well as non-technical community managers and decision makers, we need usable and powerful tools that support social media data analysis.
- Thanks to Marc Smith for the slideName as many connections (ties) as you can between people on Facebook or Pinterest.
- Thanks to Marc Smith for the slide
- This is my personal facebook network. Noticed those with high betweenness centrality (images and larger), clear clusters identified by a clustering algorithm that match up well with the descriptions I added later.
- Thanks to Marc Smith for this slide
- Thanks to Marc Smith for his slides.
- Options for getting network data