Downloaded 15 times
![[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
6 kinds of Twitter social media networks](https://image.slidesharecdn.com/nodexlworkshopfinal-170109092334/75/An-Introduction-to-NodeXL-for-Social-Scientists-26-2048.jpg)
![[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
6 kinds of Twitter social media networks](https://image.slidesharecdn.com/nodexlworkshopfinal-170109092334/75/An-Introduction-to-NodeXL-for-Social-Scientists-27-2048.jpg)
Uploaded byDr Wasim Ahmed
An Introduction to NodeXL for Social Scientists
Wasim Ahmed is a PhD candidate at the University of Sheffield focused on social network analysis (SNA), with applications in various fields including computer science, sociology, and law enforcement. The document outlines key concepts in SNA such as nodes, edges, and centrality measures, along with methodologies for analyzing social media networks. It also emphasizes the importance of ethical considerations in academic research and introduces tools like NodeXL for visualizing social networks.
More Related Content
Similar to An Introduction to NodeXL for Social Scientists
An Introduction to NodeXL for Social Scientists
- 2. About Me Wasim Ahmed(BA, MSc) PhD candidate (Faculty Scholarship) Information School, University of Sheffield Member of Social Media Research Foundation @was3210 [email protected] https://wasimahmed.org/
- 3.
- 4. SNA Applications • Usedacross a wide range of disciplines here are some: – Academic and Industry uses especially in computer science and sociology – Intelligence, counter-intelligence and law enforcement – Business intelligence
- 5. • Central principles –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 • 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
- 6. SNA 101 • Node –“actor” on which relationships act • 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
- 7. Recommended Reading Scott, J.(2012). Social network analysis. Sage. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press. Hansen, Shneiderman, Smith (2011) Analysing Social Media Networks with NodeXL Insights from a Connected World
- 8. Centrality – Centrality measureshelp address the question: who the most important or central person in this network? – Centrality measures include: • Degree centrality • Closeness centrality • Betweenness centrality • Eigenvector centrality • PageRank centrality
- 9. Betweenness Centrality From RichardIngram’s blog post visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is- believing/
- 10. Degree Centrality From RichardIngram’s blog post visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is- believing/
- 11. In New Methodologiesfor Researching News Discussion on Twitter Axel Bruns & Jean Burgess (2012) describe a number of techniques of analysing Twitter data such as : • Activity patterns over time i.e., time series analysis • Key users • Mentions of key users and key actors overtime • Advanced Network Analysis A method of analysing Twitter
- 12.
- 13. • Most frequentlyshared URLs, Domains, Hashtags, Words, Word Pairs, Replied-To, Mentioned Users, and most frequent tweeters • Produces metrics overall and by group of users (users are grouped by tweet content) • By looking at different metrics associated with different groups (G1, G2, G3 etc) you can see the different topics that users may be talking about Produces a number of metrics
- 14. Crowds matter • Whencrowds gather on the streets people take pictures • Can use the analogy of network graphs as a way of taking pictures of online crowds
- 15. Patterns are left behind 15 Whenusers engage online
- 16. Social Media (email, Facebook,Twitter, YouTube, and more) is all about connections from people to people. 16
- 17. There are manykinds of ties…. Send, Mention, http://www.flickr.com/photos/stevendepolo/3254238329 Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
- 18. World Wide Web Socialmedia must contain one or more social networks
- 20. Mapping and MeasuringConnections with Like MSPaint™ for graphs. — the Community
- 21. A goal ofSMRF: make SNA easier • Existing Social Network Tools are challenging for novice users • Tools like Excel are widely used • Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display
- 22. NodeXL Ribbon inExcel
- 23. NodeXL imports “edges”from social media data sources
- 25.
- 26. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] BrandClusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
- 27. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] BrandClusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
- 28. New York TimesArticle Paul Krugman Broadcast: Audience + Communities
- 29. What SMRF havedone: Open Data • NodeXLGraphGallery.org – User generated collection of network graphs, datasets and annotations – Collective repository for the research community – Published collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance
- 31. Ethics • In anacademic context uploading to the graph gallery may not be permitted as participants are personally identifiable • It is possible to use NodeXL to create offline graphs and to report aggregately • Known cases of academics using data from the graph gallery by gaining ethical approval
- 32.
- 33.
- 34.
- 35.
- 36.
- 37. A project fromthe Social Media Research Foundation: http://www.smrfoundation.org