Tutorial presentation at the Global Investigative Journalism Conference 2019 in Hamburg - Mapping Virtual Crowd Networks for Journalists - Application and Examples

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)

2. 2

3. 3

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

6. VISUALIZATION: GROUP-IN-A-BOX 6
Force-DirectedTreemap

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

13. 13
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=84200
Search term: dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt
https://www.dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt/

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

31. 31
Bundestag:
Oct-12-2018
Internal Network

32. Bundestag:
Oct-12-2018
Full Network
32

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

48. HASHTAG NETWORK
48

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