Workshop on Data Collection & Network Analysis with @Netlytic & the iGraph R Package
4 likes3,521 views
The document summarizes a workshop on analyzing social media data through network analysis. It discusses using tools like Netlytic and the iGraph R package to collect and analyze social network data from platforms like Twitter. The workshop covers identifying nodes and ties in networks, comparing approaches like chain and name networks, and provides an example analysis of a Twitter hashtag network.
![• Common approach: surveys or interviews
• A sample question about students’ perceived social structures
How Do We Collect Information About Social Networks?
Please indicate on a scale from [1] to [5],
YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS
[1] - don’t know this person
[2] - just another member of class
[3] - a slight friendship
[4] - a friend
[5] - a close friend
Alice D. [1] [2] [3] [4] [5]
…
Richard S. [1] [2] [3] [4] [5]
Source: C. Haythornthwaite, 1999
Anatoliy Gruzd Twitter: @gruzd 15](https://image.slidesharecdn.com/gruzdnetlyticsms15july272015-150728025229-lva1-app6892/85/Workshop-on-Data-Collection-Network-Analysis-with-Netlytic-the-iGraph-R-Package-13-320.jpg)
![Automated Discovery of Social Networks
Approach 2: Name Network
FROM: Ann
“Steve and Natasha, I couldn't wait to see your site.
I knew it was going to [be] awesome!”
This approach looks for personal names in the content of the messages to
identify social connections between group members.
22](https://image.slidesharecdn.com/gruzdnetlyticsms15july272015-150728025229-lva1-app6892/85/Workshop-on-Data-Collection-Network-Analysis-with-Netlytic-the-iGraph-R-Package-20-320.jpg)
Workshop on Data Collection & Network Analysis with @Netlytic & the iGraph R Package
- 1. Workshop 1A: Data Collection & Network Analysis with @Netlytic & the iGraph R Package Anatoliy Gruzd gruzd@ryerson.ca @gruzd Associate Professor, Ted Rogers School of Management Director, Social Media Lab Ryerson University #SMSociety15 Toronto, July 27, 2015
- 2. Outline • Making Sense of Social Media Data • Practice Part 1: Netlytic • Practice Part 2: R + igraph Anatoliy Gruzd 3
- 3. Growth of Social Media and Social Networks Data Facebook 1B+ users Twitter 500M+ usersSocial Media have become an integral part of our daily lives!
- 4. How to Make Sense of Social Media Data? Anatoliy Gruzd Twitter: @gruzd 5
- 5. Social Big Data -> Visualizations -> Understanding (Development, Application & Validation) How to Make Sense of Social Media Data? Anatoliy Gruzd Twitter: @gruzd 6
- 6. How to Make Sense of Social Media Data? Social Network Analysis (SNA) • Nodes = People • Edges /Ties (lines) = Relations/ “Who retweeted/ replied/ mentioned whom” Anatoliy Gruzd Twitter: @gruzd 7
- 7. Studying Online Social Networks http://www.visualcomplexity.com/vc • Forum networks • Blog networks • Friends’ networks (Facebook, Twitter, Google+, etc…) • Networks of like-minded people (YouTube, Flickr, etc…) Anatoliy Gruzd Twitter: @gruzd 8
- 8. • Reduce the large quantity of data into a more concise representation • Makes it much easier to understand what is going on in a group Advantages of Social Network Analysis Once the network is discovered, we can find out: • How do people interact with each other, • Who are the most/least active members of a group, • Who is influential in a group, • Who is susceptible to being influenced, etc… Anatoliy Gruzd Twitter: @gruzd 9
- 10. Anatoliy Gruzd Twitter: @dalprof2012 Olympics in London
- 11. Anatoliy Gruzd Twitter: @dalprof #tarsand Twitter Community White, B., Castleden, H., & Gruzd, A. (2015). Talking to Twitter users: Motivations behind Twitter use on the Alberta oil sands and the Northern Gateway Pipeline. First Monday, 20(1). DOI: 10.5210/fm.v20i1.5404
- 12. Common approach for collecting social network data: • Self-reported social network data may not be available/accurate • Surveys or interviews Problems with surveys or interviews • Time-consuming • Questions can be too sensitive • Answers are subjective or incomplete • Participant can forget people and interactions • Different people perceive events and relationships differently How Do We Collect Information About Online Social Networks? Anatoliy Gruzd Twitter: @gruzd 14
- 13. • Common approach: surveys or interviews • A sample question about students’ perceived social structures How Do We Collect Information About Social Networks? Please indicate on a scale from [1] to [5], YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS [1] - don’t know this person [2] - just another member of class [3] - a slight friendship [4] - a friend [5] - a close friend Alice D. [1] [2] [3] [4] [5] … Richard S. [1] [2] [3] [4] [5] Source: C. Haythornthwaite, 1999 Anatoliy Gruzd Twitter: @gruzd 15
- 14. Goal: Automated Networks Discovery Challenge: Figuring out what content-based features of online interactions can help to uncover nodes and ties between group members How Do We Collect Information About Online Social Networks? 16
- 15. Automated Discovery of Social Networks Emails Nick Rick Dick • Nodes = People • Ties = “Who talks to whom” • Tie strength = The number of messages exchanged between individuals 17
- 16. Automated Discovery of Social Networks “Many to Many” Communication ChatMailing listservForum Comments 18
- 17. Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to) FROM: Sam PREVIOUS POSTER: Gabriel .... .... .... Posting header Content 19
- 18. Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to) FROM: Sam PREVIOUS POSTER: Gabriel “ Nick, Gina and Gabriel: I apologize for not backing this up with a good source, but I know from reading about this topic that … ” Posting header Content Possible Missing Connections: • Sam -> Nick • Sam -> Gina • Nick <-> Gina 20
- 19. 21 Chain Networks: missed info. FROM: Eva REFERENCE CHAIN: Gabriel, Sam, Gina “ Gina, I owe you a cookie. This is exactly what I wanted to know. I was already planning on taking 402 next semester, and now I have something to look forward to! ” FROM: Fred “ I wonder if that could be why other libraries around the world have resisted changing – it's too much work, and as Dan pointed out, too expensive. ” Ex.2 Ex.3 21
- 20. Automated Discovery of Social Networks Approach 2: Name Network FROM: Ann “Steve and Natasha, I couldn't wait to see your site. I knew it was going to [be] awesome!” This approach looks for personal names in the content of the messages to identify social connections between group members. 22
- 21. Chain Network (less connections) Name Network (more connections) Comparing Chain vs Name Networks Example: Youtube comments Chain Network Name Network 23
- 22. • Main Communicative Functions of Personal Names (Leech, 1999) – getting attention and identifying addressee – maintaining and reinforcing social relationships • Names are “one of the few textual carriers of identity” in discussions on the web (Doherty, 2004) • Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005) Automated Discovery of Social Networks Approach 2: Name Network 24
- 23. Automated Discovery of Social Networks Name Network Method: Challenges Kurt Cobain, a lead singer for the rock band Nirvana chris is not a group member Santa Monica Public Library John Dewey, philosopher & educator mark up language Solution: - Name alias resolution 25
- 24. Example: Twitter Networks @John @Peter @Paul • Nodes = People • Ties = “Who retweeted/ replied/mentioned whom” • Tie strength = The number of retweets, replies or mentions How to Make Sense of Social Media Data? 26
- 25. Automated Discovery of Social Networks Twitter Data Example 27 Chain Network ties Name Network ties none @Cheeflo -> @JoeProf @Cheeflo -> @VMosco
- 26. Automated Discovery of Social Networks Twitter Data Example 28 Chain Network ties Name Network ties @gruzd -> @sidneyeve @gruzd -> @sidneyeve
- 27. Comparing Chain vs Name Networks Example: Twitter data - #SMSociety15 hashtag Chain Network Name Network 10 nodes, 19 ties 105 nodes, 152 ties
- 28. Anatoliy Gruzd Netlytic.org cloud-based research infrastructure for automated text analysis & discovery of social networks from social big data Networks Stats Content 30
- 29. Tutorial: Analyzing #SMSociety15 on Twitter https://netlytic.org/home/?p=10676 Anatoliy Gruzd 31
- 30. Social Media Research Toolkit maintained by the Social Media Lab http://socialmedialab.ca/?page_id=7801 Anatoliy Gruzd 34 TOOLS