GIJC19 - NodeXL Tutorial - Session 2
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The document provides a comprehensive tutorial on mapping virtual crowd networks for journalists using NodeXL, focusing on key features such as network and content analysis, visualization, and automation. It discusses various metrics, network shapes, and examples of data analysis related to significant social media events, particularly on Twitter. Additionally, it outlines insights from social network analysis regarding Twitter usage in the German Bundestag and includes multiple resources and links for further exploration.
![[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
Retweet/Spam Bot
NETWORK SHAPES 7](https://image.slidesharecdn.com/gijc19-nodexltutorialsession2-191108172830/85/GIJC19-NodeXL-Tutorial-Session-2-7-320.jpg)
![[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
NETWORK SHAPES 8](https://image.slidesharecdn.com/gijc19-nodexltutorialsession2-191108172830/85/GIJC19-NodeXL-Tutorial-Session-2-8-320.jpg)
![▪ Borgatti, Stephen P. (2006): Identifying sets of key players in a social network. In: Comput Math Organiz Theor (2006) 12:
21–34 [DOI 10.1007/s10588-006-7084-x]
▪ Castells, Manuel (1996): The Rise of the Network Society, Malden: Blackwell Publishers.
▪ Aaron Clauset, M. E. J. Newman, and Cristopher Moore (2004): Finding community structure in very large networks. In:
Phys. Rev. E 70.
▪ Dicken, Peter (2011): Global Shift – Mapping the Changing Contours of the World Economy (6th edition). Sage Publications.
▪ Litterio, Arnaldo M., et. al. (2017): "Marketing and social networks: a criterion for detecting opinion leaders", European
Journal of Management and Business Economics, Vol. 26 Issue: 3, pp.347-366, https://doi.org/10.1108/EJMBE-10-2017-
020
▪ Frank W. Takes, Eelke M. Heemskerk (2016): Centrality in the global network of corporate control. Social Network Analysis
and Mining, December 2016, 6:97). Online unter: https://link.springer.com/article/10.1007/s13278-016-0402-5
▪ Tingting Yan, Thomas Y. Choi, Yusoon Kim, Yang Yang (2015): A Theory of the Nexus Supplier: A Critical Supplier From A
Network Perspective. Journal of Supply Chain Management, 51-1 pp: 3-92. Online unter:
https://onlinelibrary.wiley.com/doi/abs/10.1111/jscm.12070
LITERATURE / LINKS](https://image.slidesharecdn.com/gijc19-nodexltutorialsession2-191108172830/85/GIJC19-NodeXL-Tutorial-Session-2-53-320.jpg)
GIJC19 - NodeXL Tutorial - Session 2
- 1. Mapping Virtual Crowd Networks for Journalists - Application and Examples Tutorial materials: bit.ly/nxlgijc Harald Meier harald@smrfoundation.org Hamburg – September 28th, 2019 GIJC19 - NODEXL TUTORIAL (SESSION 2)
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- 4. KEY FEATURES OF NODEXL PRO 4 Group Metrics Vertex Metrics Network Metrics Text Analysis Sentiment Analysis Top Contents Analysis Time Series Analysis 2. Network Analysis 3. Content Analysis 4. Visualization 6. Automation with Data Recipes 1. Data Import 5. Publish
- 5. SOCIAL NETWORK AND CONTENT ANALYSIS 5 Network Overview • Quantity / Density / Modularity Group Analysis • Cluster Algorithm • Density Vertex Metrics • Centrality: Betweenness, Closeness, Eigenvector, … Content Analysis • Top hashtags, words, URLs, … • Sentiment, time series Layout Algorithms • Group-In-A-Box: Treemap • Harel-Koren Fast Multiscale
- 7. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network Retweet/Spam Bot NETWORK SHAPES 7
- 8. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network NETWORK SHAPES 8
- 9. PEW Report: Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. PEW Research Report 2014: http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/ NETWORK SHAPES Video: SMRF Director Marc Smith | Network Mapping the Ecosystem: https://www.youtube.com/watch?v=kDiGl-2m868 9
- 10. TWITTER NETWORK DATA PERSPECTIVES Twitter Search API: Open space 10 Twitter Users API: Restricted space ▪ past 10 days / max. 18,000 tweets per query ▪ max. 3,200 tweets per user
- 11. 11 #ddj Twitter NodeXL SNA Map and Report for Tuesday, 24 September 2019: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=210841 Summarize and explore Search term: #ddj
- 12. 12 @nytimes Twitter NodeXL SNA Map and Report for Tuesday, 24 September 2019: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=210833 url:nytimes Twitter NodeXL SNA Map and Report for Tuesday, 24 September 2019: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=210831 Search term: url:nytimes Search term: @nytimes
- 14. 14 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=209614 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=209628 German Talkshow Illner Sep-12-2019 - 22:15-23:15 Search term: Illner - Before / after the show - Live data
- 15. 15 Sports Coverage: EURO 2016 – June 27, 2016 Search term: #ITAESP
- 16. ltwbb OR ltwbb19 until:2019-08-31: http://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208480 16 Elections: Landtagswahlen Brandenburg Search term: ltwbb OR ltwbb19 until:2019-08-31
- 17. 17 list:Europarl_EN/all-meps-on-twitter Monday, 20 May 2019: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=197547 Members of the European Parliament before the European Elections Color by Language Search term: list:Europarl_EN/all-meps- on-twitter
- 18. 18 Tesla lang:en Twitter NodeXL SNA Map and Report for Friday, 07 June 2019: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=199387 Visualizing Sentiment Color by Language Search term: Tesla lang:en
- 19. TWITTER SEARCH NETWORK COMPARISON 19 Tesla Twitter Brand Network 2018-03-26 https://nodexlgraphgallery.org/Pages/ Graph.aspx?graphID=146166 Positive Words: 3.83% Negative Words: 1.41% Tesla Twitter Brand Network 2018-05-07 https://nodexlgraphgallery.org/Pages/ Graph.aspx?graphID=150936 Positive Words: 2.38% Negative Words: 2.56% Tesla Twitter Brand Network 2018-05-24 https://nodexlgraphgallery.org/Pages /Graph.aspx?graphID=152963 Positive Words: 2.60% Negative Words: 2.93% Tesla Twitter Brand Network 2018-06-01 https://nodexlgraphgallery.org/Pages /Graph.aspx?graphID=155439 Positive Words: 3.12% Negative Words: 1.72%
- 20. USER ACCOUNT INVESTIGATION 20 https://www.vanityfair.com/news/2017/06/trump-says-he-wont-stop-tweeting https://theguardiansofdemocracy.com/trump-schedules-last-minute-meetings-based-whatever-saw-fox-friends-report/
- 21. TWITTER USERS NETWORK: EGO NETWORK 1.0 realdonaldtrump Userlist 1000 2019-06-09 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=199512 21
- 22. TWITTER USERS NETWORK: EGO NETWORK 2.0 22
- 23. TWITTER USERS NETWORK: EGO NETWORK 2.0 Based on Twitter users followed by @realdonaldtrump https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=174922 23
- 24. Social Network Analysis – Insights from mapping the Twitter network of the German Bundestag
- 25. 19. BUNDESTAG: TWITTER USEAGE 25 Party Color Seats Twitter users Twitter users per seat CDU/CSU 246 131 53 % SPD 153 123 80 % AfD 92 85 92 % FDP 80 72 90 % Die Linke 69 60 87 % B90/Die Grünen 67 64 96 % no affiliation 2 2 100 % All 709 537 76 % https://www.bundestag.de/parlament/plenum/sitzverteilung_19wp
- 26. NETWORK MAPS CREATED FROM ONE DATASET 26 Internal Network Map Party Network Map Party Interaction MapInfluencer Layout Full Network Map Party Cluster Map
- 27. TWEETING STRATEGIES BY PARTY Network Edge Relationships: ▪ Tweet → No interaction ▪ Retweet → Amplification ▪ Replies to → Talking to someone ▪ Mentions → Talking about someone 27 Dataset: 1000 tweets per User / Sep 2, 2019 / 581,088 network edges
- 28. TWEETING STRATEGIES: @C_LINDNER (FDP) 28 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208710 Vertices : 884 Relationship: Mentions : 1316 Replies to : 663 Tweet : 123 Retweet : 112
- 29. TWEETING STRATEGIES: @KAHRS (SPD) 29 http://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208819 Vertices : 457 Relationship: Replies to : 847 Mentions : 488 Tweet : 76 Retweet : 52
- 30. TWEETING STRATEGIES: @UDOHEMMELGARN (AFD) 30 http://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208719 Vertices : 302 Relationship: Retweet : 503 Mentions : 434 Replies to : 323 Tweet : 215
- 33. PARTY NETWORK: B90/DIE GRÜNEN 33 https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208679
- 34. PARTY NETWORK: FDP 34 https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=208695
- 35. PARTY INTERACTION MAPS 35 June 2019 July 2019 August 2019
- 36. INTERNAL NETWORK INFLUENCERS – AUGUST 2019 36 Rank Twitter Handle Party In-Degree Out- Degree Betweenness Centrality Eigenvector Centrality 1 peteraltmaier CDU/CSU 52 6 13315.978 0.017 2 heikomaas SPD 45 1 11125.246 0.009 3 udohemmelgarn AfD 5 40 10782.212 0.007 4 c_lindner FDP 49 19 10681.718 0.018 5 kahrs SPD 32 44 9697.306 0.014 6 abaerbock B90/Die Grünen 39 10 8240.197 0.012 7 katjakipping Die Linke 28 10 8047.848 0.005 8 paulziemiak CDU/CSU 38 8 7991.705 0.010 9 baerbelbas SPD 12 31 7364.514 0.006 10 olliluksic FDP 17 39 6588.962 0.016 11 tschipanski CDU/CSU 9 36 6326.758 0.010 12 cem_oezdemir B90/Die Grünen 34 16 6053.835 0.011 13 andischeuer CDU/CSU 38 3 6043.266 0.013 14 eskensaskia SPD 14 35 5943.170 0.012 15 th_sattelberger FDP 10 33 5113.055 0.011 Betweenness Centrality Eigenvector Centrality
- 37. EXTERNAL NETWORK INFLUENCERS – AUGUST 2019 Rank Twitter Handle Category Indegree Betweenness Centrality 1 welt News/Media 115 1988243.028 2 spdde Party 144 1970892.003 3 spdbt Party 128 1915653.728 4 die_gruenen Party 109 1723811.824 5 akk Politician 124 1556913.568 6 dielinke Party 85 1466186.103 7 cdu Party 114 1266732.958 8 spiegelonline News/Media 81 1258353.580 9 cducsubt Party 100 1202502.309 10 gruenebundestag Party 84 1081743.754 11 olafscholz Politician 101 1039363.609 12 tagesspiegel News/Media 64 851519.692 13 linksfraktion Party 64 779983.049 14 tagesschau News/Media 65 754499.804 15 faznet News/Media 73 748936.554 16 fdp Party 78 661145.616 17 afd Party 63 639764.922 18 sz News/Media 60 614854.062 19 mpkretschmer Politician 75 596165.392 20 tazgezwitscher News/Media 49 548448.097 The most influential Twitter users outside the Bundestag are related national party accounts, large news media outlets and politicians without a seat in the Bundestag. 37
- 38. BOT NETWORK: SEARCH NETWORK #NATGAS 38 http://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=102873
- 39. BOT NETWORK: #NATGAS USER NETWORK - INTERNAL 39 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=102889
- 40. BOT NETWORK: #NATGAS USER NETWORK 40 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=102887
- 41. BOT NETWORK: NODEXL „6 TYPES OF“ 41 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=160416
- 42. 42MULTIMODAL NETWORKS: USER - MEDIA NodeXL types networks Twitter User-Image Network - treemap - grid layout 2019-02-07 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=185727
- 43. 43MULTIMODAL NETWORKS: USER - MEDIA #Tesla Twitter User-to-Media File Network G1-G22 Monday, 04 February 2019 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=185277
- 44. 44MULTIMODAL NETWORKS: USER - DOMAIN #Tesla Twitter User-to-Domain Network Report for Monday, 04 February 2019 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=185264
- 45. 45CROSS-PLATFORM ANALYSIS: TWITTER-YOUTUBE Bolsonaro (youtube OR youtube.com OR youtu.be) user-url network until:2018-10-28 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=185579
- 46. 46CROSS-PLATFORM ANALYSIS: TWITTER-FACEBOOK Bolsonaro (facebook.com OR fb.com OR fb.me) until:2018-10-22 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=185533
- 47. 47CROSS-PLATFORM ANALYSIS: TWITTER-INSTAGRAM Bolsonaro Instagram until:2018-10-26 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=188573
- 49. EMAIL NETWORKS 49 https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=210675 Enron Email Network
- 50. NODEXL GRAPH GALLERY 1 NodeXLGraphGallery.org: NodeXL Pro network maps, reports and options
- 51. NODEXL GRAPH GALLERY 51
- 52. Social Media Research Foundation and NodeXL ▪ Social Media Research Foundation: http://www.smrfoundation.org/ ▪ NodeXL Graph Gallery: https://nodexlgraphgallery.org/ ▪ Marc Smith | Network Mapping the Ecosystem: https://www.youtube.com/watch?v=kDiGl-2m868 ▪ How to Automate NodeXL Pro: https://www.youtube.com/watch?v=mjAq8eA7uOM ▪ Eduarda Mendes Rodrigues, Natasa Milic-Frayling, Marc Smith, Ben Shneiderman, Derek Hansen (2011): Group-in-a-box Layout for Multi-faceted Analysis of Communities. In: IEEE Third International Conference on Social Computing, October 9- 11, 2011. Boston, MA: https://www.cs.umd.edu/hcil/trs/2011-24/2011-24.pdf ▪ Smith, Marc A., Lee Rainie, Ben Shneiderman and Itai Himelboim (2014): Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. PEW Research Report: https://www.pewinternet.org/2014/02/20/mapping-twitter-topic- networks-from-polarized-crowds-to-community-clusters/ ▪ Derek Hansen, Ben Shneiderman and Marc Smith (2009): Analyzing Social Media Networks with NodeXL: https://www.elsevier.com/books/analyzing-social-media-networks-with-nodexl/hansen/978-0-12-382229-1 ▪ Itai Himelboim, Marc A. Smith, Lee Rainie, Ben Shneiderman and Camila Espina: Classifying Twitter Topic-Networks Using Social Network Analysis. In: Social Media + Society (January-March 2017: 1 –13). https://journals.sagepub.com/doi/full/10.1177/2056305117691545 LITERATURE / LINKS
- 53. ▪ Borgatti, Stephen P. (2006): Identifying sets of key players in a social network. In: Comput Math Organiz Theor (2006) 12: 21–34 [DOI 10.1007/s10588-006-7084-x] ▪ Castells, Manuel (1996): The Rise of the Network Society, Malden: Blackwell Publishers. ▪ Aaron Clauset, M. E. J. Newman, and Cristopher Moore (2004): Finding community structure in very large networks. In: Phys. Rev. E 70. ▪ Dicken, Peter (2011): Global Shift – Mapping the Changing Contours of the World Economy (6th edition). Sage Publications. ▪ Litterio, Arnaldo M., et. al. (2017): "Marketing and social networks: a criterion for detecting opinion leaders", European Journal of Management and Business Economics, Vol. 26 Issue: 3, pp.347-366, https://doi.org/10.1108/EJMBE-10-2017- 020 ▪ Frank W. Takes, Eelke M. Heemskerk (2016): Centrality in the global network of corporate control. Social Network Analysis and Mining, December 2016, 6:97). Online unter: https://link.springer.com/article/10.1007/s13278-016-0402-5 ▪ Tingting Yan, Thomas Y. Choi, Yusoon Kim, Yang Yang (2015): A Theory of the Nexus Supplier: A Critical Supplier From A Network Perspective. Journal of Supply Chain Management, 51-1 pp: 3-92. Online unter: https://onlinelibrary.wiley.com/doi/abs/10.1111/jscm.12070 LITERATURE / LINKS
- 54. SOCIAL MEDIA RESEARCH FOUNDATION 54 https://www.smrfoundation.org/ https://www.nodexlgraphgallery.org/ Book: Derek Hansen, Ben Shneiderman and Marc Smith (2020): Analyzing Social Media Networks with NodeXL: https://www.elsevier.com/books/analyzing-social-media- networks-with-nodexl/hansen/978-0-12-817756-3