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Basics of network analysis using Netlytic


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A beginner’s guide to social network analysis for social media and strat comm professors.
From a social network analysis fan with much to learn!
Overview of how to use the network visualization tool
Tutorial for using Netlytic:

Additional Resources
♣ Basics of social network analysis slides
♣ Blog post “A Quick, Interactive Activity for Introducing the Concept of Digital Influencers”:
♣ Blog post detailing the below assignment:

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Basics of network analysis using Netlytic

  1. 1. Social Network Analysis Basics for Social Media Professors Dr. Matthew J. Kushin Department of Communication Shepherd University
  3. 3. Who talks to whom?
  4. 4. Influencers • Not everyone on social media is equal in terms of influence
  6. 6. Social Networks • Represent the connections between points of interest. – Often shown visually.
  7. 7. Key Terms Nodes – entities evaluating (people, social network profiles, pages, groups). Edges – connections between them (likes, following, friendship, mentions). Node
  8. 8. Key Terms Edges represent who is connected to whom. Bob knows Sally and John Bob Sally John
  9. 9. Directional • An edge can be directional. John Sally Examples: 1) Sally mentioned John in an Instagram comment 2) Sally emailed John. 3) Sally Tweeted John. 4) Sally has a crush on John (but he doesn’t have one back).
  10. 10. Uni-Directional • Or, an edge can be uni-directional. John Sally Examples: 1) Sally and John know each other. 2) Sally and John emailed back and forth. 3) Sally said she has a crush on John and John said he has a crush on Sally.
  11. 11. • Centrality is a measure of: – “What characterizes an important node (e.g., person) in a network?” • Note: Many other stats used to analyze network. We are only scratching the surface.
  12. 12. Degree Centrality • Several types of centrality exist. – We’ll look at: Degree centrality • The # of ties a node has. This is the likelihood of info flowing through this person. – Note: Only considers walks of length of 1 (that is, from 1 node to the other) #6 has 1 tie. #2 has 3 ties.
  13. 13. 2 Types • In-Degree: # of ties directed to the node. • I.e., # of people mentioning that person in a Tweet. – Indicates Popularity / prominence
  14. 14. Has 32 in-degree edges. Why? 32 other Twitter accounts mentioned @ShepherdU in a Tweet. Indicating it is popular – talked about often. Network of hashtag: #ShepherdU
  15. 15. 2 Types • Out-degree - # of ties the node directs to others. – i.e., gregariousness. All the people I Tweet to, RT, or mention.
  16. 16. Out-degree • Significance? – Indicates involved/care about topic • Indicates they are active about a topic. – Could be an influencer: • May indicate influence (may not, as no one may respond or pay attention.)
  17. 17. @ShepherdU has 0 out degree edges. Why? It hasn’t mentioned anyone in any Tweets. Indicating, it doesn’t often a) Tweet at people or 2) RT others. Primarily used to send out communication. Network of hashtag: #ShepherdU
  18. 18. These are 3 OUTDEGREE connections because: @khattakfugan sent the Tweet. She mentions @UGRADPakistan (by Retweeting them) and @IREXintil and @ShepherdU Network of hashtag: #ShepherdU
  19. 19. Other Stats • Reciprocity – Proportion of ties showing two-way communication • Calculated: Reciprocal ties / total number of ties – High reciprocity (approaching 1) = lots of 2- way convos – Low (approaching 0) = one-way convos.
  20. 20. Other Key Stats • Centralization – average degree centrality of all nodes. – High centralization (closer to 1) = a few central people are dominating information flow. • Example: Graduation Ceremony – Low (closer to 0) = info flows freely. • Example: Party after graduation ceremony.
  21. 21. Other Key Stats • Diameter – longest distance between 2 network participants. – Indicates: network’s size. • Ex: • For info to pass from John to Trevor, it has to go through: – John - > Sally - > Billy -> Robin -> Trevor • Diameter = 5.
  22. 22. Other Key Stats • Density – How closely connected is this network? – Shows speed information can flow. • Calculated: # of existing ties / # of possible ties. – Dense networks: More close-knit • Many people talking to one-another. – Not dense: • Few people talking to one another. • A density score close to 1 is very dense. • A density score close to 0 lacks density.
  23. 23. Example: Density • This network has high density (but not perfect density) because most people are talking to each other.
  24. 24. Diameter – 3 people is farthest distance between communicators. Centralization – Somewhat around ShepherdU – dominate as the topic of discussion. Reciprocity – few people talking to one another. Density low – Only a few people talk to each other in this network.
  25. 25. APPLICATIONS: METRICS Understanding Social Media Conversations
  26. 26. Analyzing Social Media • Helps us understand interactions online. Who Tweets, mentions, etc., whom.
  27. 27. Measuring Online Networks • For hashtags or search terms: – Outdegree: Who posts a lot? • Represents gregariousness, and may indicate: – Passion/interest – Influence / Thought leadership – In Degree: Who is popular in this network? • Ex: – High mentions – People whose content is being RT’d. – What clusters of people are talking to one another, and what about?
  28. 28. • For mentions of your client – What communities (who) is talking about your client’s @username? • i.e., what can you find out about who these groups of interacting people are? – Who all is mentioning (talking about) your client the most? • What are these folks saying?
  29. 29. #CincoDeMayo Tweet by Bleacher Report mentioning @SHAQ Why is Shaq being talked about during Cinco De Mayo!?
  30. 30. Considering… • We can see a lot of people are sharing humorous or other #cindodemayo related content. • Not having conversations • Density – SUPER Low – Almost no one is connected to one- another • Reciprocity – very low – Not a lot of people talking to one-another. Mostly sharing, RTs each other. • Centralization – very low – Info flowing freely (not dominated by a few).