Uploaded byMichael Lieberman
Visualizing Big Data - Social Network Analysis
This document discusses social network analysis (SNA) and its applications in visualizing connections and interactions on social media platforms like Twitter and Facebook. It covers the history, major forms of social networks, and various measures of centrality to understand network dynamics. It provides practical insights into how SNA can enhance the effectiveness of networks and brand research.
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Visualizing Big Data - Social Network Analysis
- 1. Visualizing Big Data: SocialNetwork Analysis By Michael Lieberman
- 2. SNA 101 • Socialmedia network maps visualize the patterns of connection that form when people follow, reply and mention one another in Internet communication services like Twitter. • When analyzed in aggregate collections of individual connections form web-like network structures. • Network structures form the nature of the conversation. • Social media network structures come in several major forms: • Divided • Unified • Fragmented • Clustered
- 3. Social Networks • History: from the dawnof time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wik ipedia.org/wi ki/Jacob_L._ Moreno Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football team. Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing Company.
- 4. The NSA UsesSocial Network Analysis To Map Terrorist Networks
- 5. Why Social NetworkVisualizations • Whenever you are studying a social network • When you wish to understand how to improve the effectiveness of the network • Visualize your data so as to uncover patterns in relationships or interactions • When you want to follow the paths that information follows in social networks • When you do quantitative or branding research
- 6. World Wide Web Eachcontains one or more social networks
- 7. Interpretation of measures CNMSocial Media Module – Giorgos Cheliotis (gcheliotis@nus.edu.sg)7 Degree Betweenness Closeness Eigenvector How many people can this person reach directly? How likely is this person to be the most direct route between two people in the network? How fast can this person reach everyone in the network? How well is this person connected to other well- connected people? Centrality measure Interpretation in social networks
- 9. Social Network Maps •Networks • Tie Strength • Key Players • Cohesion • Influencers 9 How to represent various social networks How to identify strong/weak ties in the network How to identify key/central nodes in network Measures of overall network structure Sub-groups Bridges Islands CNM Social Media Module – Giorgos Cheliotis (gcheliotis@nus.edu.sg)
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- 11. 6 kinds ofTwitter social media networks
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- 14. #BMW Brand / PublicTopic
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- 16. Ann Handley @marketingprofs Broadcast: Audience+ Communities
- 17. #Hashtag Clusters inMap Show Brand Snapshot on Twitter Word pairs highlight Brand Conversation Contrasting #Hashtags @WendyDavis Top Hashtags in Tweet in G1: Top Hashtags in Tweet in G2: Top Hashtags in Tweet in G8: WendyDavis WendyDavis WendyDavis SingleMomLife tcot LizWarren dfw caring tcot FauxLife TEXAS unitered TeamWendy msnbc prostitute caring prolife lies PointBreak badmom MoreFakeThanWendyDavis dem KellyFile GregAbbott prolife ccot prolife Warren AbortionBarbie KeepTexasRed Top Word Pairs in Tweet in G1:Top Word Pairs in Tweet in G2:Top Word Pairs in Tweet in G8: rt,dloesch court,order rt,rednationrising single,mom order,use kellyfile,more dloesch,wendydavis use,drugs more,wendydavis wendydavis,attack drugs,before wendydavis,story attack,personal before,seeing story,divorced personal,story seeing,kids divorced,man story,life kids,reconsider man,paid life,thing reconsider,political paid,education thing,story political,aspirations education,immediately mom,worked aspirations,wendydavis immediately,graduation
- 18. Social Network Analysisand Facebook Michael Lieberman’s Facebook Network • G1 are High School Classmates • G2 Current Friends – Jerusalem • G3 Family • G9 Worked at Ristorante Alfredo 1989 Hunterdon Central Class of 1982 Social Circle Extended Family Ristorante Alfredo 1989 For Facebook accounts passwords are required.
- 19. Facebook Like’s Pages- Starbucks Comments Made on the Starbucks ‘Likes’ 1/21/2014 – 1/23/2014 Graph Metric Value Graph Type Undirected Vertices 11777 Unique Edges 365085 Edges With Duplicates 0 Total Edges 365085
- 20. Facebook Like’s Pages- Starbucks Comments -- Starbucks ‘Likes’ 1/21/2014 – 1/23/2014 Name Locale Comments Between Centrality Eigenvector Centrality Agnes Pylko en_US I quite like that-I am quite enjoying Starbucks tea these days 0.743626069 0.754179725 Irma Ofelia Rodriguez en_GB Que sabroso como estas como estan los ninos saludos Lupita. 0.68678461 0.623989193 Somer Alderson en_GB You weren't open when I was gonna to get coffee tonight and it made me sad. Darn campus store. 0.17252211 0.481296922 Karen Craven en_US I love there expresso peppermint ice macho they so good karen c 0.324485608 0.397906029 Chrystal Combitsis en_US Maybe it is time to stop patronizing Starbucks. Too bad...I liked the coffee. 0.124382885 0.194781237 Kostee Hoover Nikitakis en_US god i love this specific brew. LOVE IT I TELL YA 0.796447418 0.157976213 Barbara Saline en_US I bought Starbuck individual cup coffee this week at Cosco, absolutely delicious and a great price 0.125920577 0.129647526 Tom Wood en_US Oh, Starbucks - I wish you delivered. 0.651564928 0.127384168 Anne Gormley en_US Looks like the Melita filter and funnel 0.354619577 0.010865484 Keller O'Rourke en_US Wasted coffee beans. 0.653199984 0.006393752 •Facebook Influencers Using Eigenvalue Centrality Measures
- 21. Mapping Hyperlink Networks •Webpages are still a company’s most prominent presence on the World Wide Web • Virtual Observatory for the Study of Online Networks (VOSON) • Set seed site (s). In VOSON, such as www.CASRO.org • VOSON finds inbound and outbound links to see sites, creates links. • Visualization of the VOSON Hyperlink Network in NodeXL shows a hyperlink network space • For example, you have a new website, how do you get potential customers to find out about your fantastic new social network analysis tool?
- 22. Hyperlink Space CASRO.org EgoHyperlink Network
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- 24. Hyperlink Space Casual DiningRestaurants
- 25. About NodeXL Introductions Marc A.Smith Chief Social Scientist Connected Action Consulting Group Marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl Michael Lieberman Multivariate Solutions michael@mvsolution.com
Editor's Notes
- #13 The network of connections among people who tweeted “#My2K” over the 1-day, 21-hour, 39-minute period from Sunday, 06 January 2013 at 03:30 UTC to Tuesday, 08 January 2013 at 01:09 UTC.
- #14 The graph represents a network of 268 Twitter users whose recent tweets contained "#cmgrchat OR #smchat. The network was obtained on Friday, 18 January 2013 at 15:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-day, 21-hour, 15-minute period from Monday, 14 January 2013 at 18:23 UTC to Friday, 18 January 2013 at 15:38 UTC.
- #15 The graph represents a network of 1,227 Twitter users whose recent tweets contained "lumia. The network was obtained on Saturday, 12 January 2013 at 19:52 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 5-hour, 1-minute period from Saturday, 12 January 2013 at 14:36 UTC to Saturday, 12 January 2013 at 19:37 UTC.
- #16 The graph represents a network of 1,260 Twitter users whose recent tweets contained "flotus". The network was obtained on Friday, 18 January 2013 at 18:26 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-hour, 3-minute period from Friday, 18 January 2013 at 15:16 UTC to Friday, 18 January 2013 at 18:20 UTC.
- #17 The graph represents a network of 399 Twitter users whose recent tweets contained "http://www.nytimes.com/2013/01/11/opinion/krugman-coins-against-crazies.html. The network was obtained on Friday, 11 January 2013 at 14:27 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 12-hour, 32-minute period from Friday, 11 January 2013 at 01:52 UTC to Friday, 11 January 2013 at 14:24 UTC.