Slides from #SMWCPH event Social Media Analytics: Concepts, Models, Methods, and Tools. For more information on the slides, please contact Professor Ravi Vatrapu at Copenhagen Business School. #smwcbsdata

1. Social Media Analytics:
Concepts, Models, Methods , & Tools
Ravi Vatrapu
Professor mso, Department of IT Management, Copenhagen Business School
Director, Computational Social Science Laboratory (CSSL),
Professor of Applied Computing, Norwegian School of Information Technology (NITH)
Email: vatrapu@cbs.dk
with
Daniel Hardt
Raghava Rao Mukkamala
Abid Hussain
Chris Zimmerman
Niels Buus Lassen
René Madsen
Kjeld Hansen
Kiran Kumar Kocherla
Social Media Week: Copenhagen
Social Media Analytics: Concepts, Models, Methods, Tools
Solbjerg Plads 3, 2.01; Frederiksberg
21-Feb-2014
1

2. WORKSHOP PROGRAM
13:00-13:30: Social Data Analytics


Concepts
Models
13:30-14:15: Empirical Studies: Methods





Social Data Analytics: US elections 2008 & Danish elections 2011
Exploratory Analysis: e-shop visits, e-shop sales, & social media actions
Correlational Analysis: revenues, social graph, & social text
Fuzzy Set Sentiment Analysis: real-world events and social media sentiments
Predictive Analytics: iPhone sales and tweets (René Madsen & Niels Buus Lassen)
14:15-14:30: BREAK
14:30-15:45: Interactive Demos: Tools



Social Data Analytics Tool (SODATO) (Abid Hussain)
Danish Sentiment Analysis Tool (Daniel Hardt)
Visual Analytics: Tableau: #Marius (Chris Zimmerman)
15:45-16:00: Plenary Discussion
Workshop Conclusion
2

3. SOCIAL MEDIA ANALYTICS: METHODOLOGICAL FRAMEWORK
3

4. TECHNOLOGIES OF PRACTICAL REASON
(FOUCAULT, M., MARTIN, L. H., GUTMAN, H., & HUTTON, P. H. (1988). TECHNOLOGIES OF THE SELF: A SEMINAR WITH MICHEL FOUCAULT: UNIV OF MASSACHUSETTS PRESS.)
Four Matrices of Practical Reason

Technologies of Production

Technologies of Sign Systems

Technologies of Power

Technologies of the Self
4

5. 5

6. 6

7. Social Computing
(Wang et al., 2007)
7

8. SOCIAL BUSINESS

”A Social Business is an organization that strategically
engages, analyses and manages social media to structure
organizational processes and support organizational functions
in order to realize operational efficiencies, generate
competitive advantages and create value for customers, share
holders and other societal stakeholders.
8

9. SOCIAL BUSINESS

Three critical aspects of Social Business
– Social Media Engagement (SmE):
Organization's strategic use of social media channels to interact with its
internal and external stakeholders for the purposes ranging from
marketing, customer support, product development and knowledge
management.
– Social Media Analytics (SmA)
Collection, storage, analysis and reporting of social data emanating from
the social media engagement of and social media conversations about the
organization.
– Social Media Management (smM)
SMM focuses on the operational issues, managerial challenges and
comparative advantages with respect to the emerging paradigm of Social
Business
9

10. Part I:
Social Data Theory
10

11. Outline of a Theory of Socio-Technical Interactions
(Vatrapu, R. (2010). Explaining culture: an outline of a theory of socio-technical interactions. Proceedings of the 3rd ACM International Conference on Intercultural Collaboration (ICIC 2010), 111-120)
 Social: other-orientation & other-involvement
 Technology: place-shifting & time-shifting
 Socio-Technical Systems involve:



Interacting with Technologies



Interacting with Technologies
Interacting with Others via Technologies
Perception of Affordances
Appropriation of Affordances
Interacting with Others via Technologies


Structures of Technological Intersubjectivity
Functions of Technological Intersubjectivity
11

12. Perception of Affordances
12

13. Appropriation of Affordances
Youtube: “Gods Must Be Crazy” + “Coke Bottle”
http://www.youtube.com/watch?v=17HGR7FBwu0
13

14. TECHNOLOGICAL INTERSUBJECTIVITY
• Production, Projection, and Performance of Intersubjectivity
• How actors interact with, relate to, and form impressions of each other
"Piled Higher and Deeper" by Jorge; www.phdcomics.com. Image used with permission.
1

15. Part II:
Social Data Models
15

16. CONCEPTUAL MODEL: SOCIAL GRAPH + SOCIAL TEXT
Technological Intersubjectivity
Appropriation of Affordances
16

17. FORMAL MODEL: SET THEORY
• Mathematics: Fuzzy Set Logic instead of Graph Theory
• Social Attribute: Associations instead of Relations
• Social Grouping: Sets instead of Social Networks
17

18. Part III:
Methods
18

19. Study #1
Social Data Analytics: Political Science
Robertson, S., Vatrapu, R., & Medina, R. (2010). Off the wall political discourse: Facebook use in the
2008 U.S. presidential election. Information Polity, 15(1,2), 11-31.
Robertson, S., Vatrapu, R., & Medina, R. (2010). Online Video “Friends” Social Networking:
Overlapping Online Public Spheres in the 2008 U.S. Presidential Election. Journal of Information
Technology & Politics, 7(2-3), 182-201. doi: 10.1080/19331681003753420
Robertson, S. P. (2011). Changes in referents and emotions over time in election-related social
networking dialog. In System Sciences (HICSS), 2011 44th Hawaii International Conference on (pp.
1-9). IEEE.
Hussain, A., Vatrapu, R., Hardt, D., & Jaffari, Z. (in press/2014). Social Data Analytics Tool: A
Demonstrative Case Study of Methodology and Software. In Gibson, R. (ed). Analysing Social Media
Data and Web Networks. Palgrave Macmillan.
19

20. FRAMEWORK: ENGAGEMENT DIMENSIONS
SOCIAL NETWORK DIALOG SPACE
Robertson, S., Vatrapu, R., & Medina, R. (2010). Off the Wall Political Discourse: Facebook Use in the 2008 U.S.
Presidential Election. Information Polity, 15(1-2), 11-31.
20

21. RESULTS: SOCIAL GRAPH: DK 2011: WALL CROSSING
21

22. SOCIAL TEXT ANALYSIS
 Reflection-to-Selection Hypothesis
 change from first-person and second-person pronoun usage
to third-person pronoun usage as an election approaches
 Converging Sentiment Hypothesis
 the amount of positive and negative discourse begins to
equalize as participants in political discourse begin to divide
their time, and their comments, between their own
candidate and group and their competitors’ candidates and
groups.
Robertson, S. (2010). Changes in Referents and Emotions Over Time in Election-Related Social Networking Dialog. Proceedings of
HICSS 2010
22

23. RESULTS: SOCIAL TEXT: US 2008: PRONOUN USAGE: OBAMA
Percentage of words in the first-person, second-person, and third-person categories for Barack Obama
Robertson, S. (2010). Changes in Referents and Emotions Over Time in Election-Related Social Networking Dialog.
Proceedings of the 44th Annual Meeting of the Hawaii International Conference on Systems Sciences (HICSS44), IEEE, .
23

24. RESULTS: SOCIAL TEXT: DK 2011: PRONOUN USAGE: HELLE
Helle Pronouns
0,03
0,025
0,02
first person
0,015
second person
third person
0,01
0,005
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Percentage of words in the first-person, second-person, and third-person categories for Helle Thorning-Schmidt
24

25. RESULTS: SOCIAL TEXT: US 2008: SENTIMENT: OBAMA
Percentage of Sentiment Words for Barack Obama
Robertson, S. (2010). Changes in Referents and Emotions Over Time in Election-Related Social Networking Dialog.
Proceedings of the 44th Annual Meeting of the Hawaii International Conference on Systems Sciences (HICSS44), IEEE, .
25

26. RESULTS: SOCIAL TEXT: DK 2011: SENTIMENT: HELLE
Helle Sentiment
0,9
0,8
0,7
0,6
0,5
Positive
0,4
Negative
0,3
0,2
0,1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Percentage of words in the first-person, second-person, and third-person categories for Helle Thorning-Schmidt
26

27. Study #2
Exploratory Analysis: e-Commerce
27

28. E-Shop Sales, Website Visits, Social Media Engagement
Monthly
7000
6000
5000
4000
3000
2000
1000
0
2013-01 2013-02 2013-03 2013-04 2013-05 2013-06 2013-07 2013-08 2013-09 2013-10 2013-11 2013-12
FBPosts
8
17
11
7
10
11
3
14
18
19
10
33
FBLike
126
199
133
68
133
170
57
291
237
188
162
280
FBComments
6
14
2
0
8
7
0
1
2
2
0
16
instagramposts
2
10
9
7
13
9
14
17
13
14
14
10
InstagramLikeCount
56
576
394
247
928
352
729
1381
839
1714
909
840
instagramCommentCount
3
34
18
6
44
12
40
64
34
124
46
26
eShop
1460,1
1269
1313,4
1370,9
1602,2
1450,8
1040,5
1528
1192,1
1057
1499,5
87,5
Sales
3809,5
4433,1
2414
4675
4375
6241,6
2742,8
5504
4213
3081
5915
399,1
Master Thesis of Julie Redlef Kristiansen, ITU
28

29. E-Shop Sales, Website Visits, Social Media Engagement
Day of the week
3000
2500
2000
1500
1000
500
0
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
FBPosts
9
72
66
58
43
72
7
FBLike
130
554
650
537
380
686
52
FBComments
14
29
107
21
12
37
4
instagramposts
1
21
24
28
28
31
5
102
1009
1347
1505
2192
2588
279
InstagramLikeCount
instagramCommentCount
0
40
98
53
118
122
20
ShopVisit
500,13
599,51
596,1
563,19
547,81
512,3
444,28
Revenue
1431,89
2065,39
2106,96
1863,62
1655,31
1517,18
1008,51
Master Thesis of Julie Redlef Kristiansen, ITU
29

30. Study #3
Correlational Analysis: revenues & facebook activity
30

31. Sales & Social: Quarterly
• Social Data Analytics Tool (SODATO) used to collect facebook data of H&M
• Time Period: 01-Jan-2009 to 31-July-2013
• Corpus:
• 100,465 posts
• 262,588 comments on posts
• 7, 779,411 likes on posts and comments
• 3,134,249 unique facebook ids/users
• 18 quarterly sales numbers
• Investigated statistical correlation between social data measures and revenues
• We found statistically significant correlations for quarterly sales with
• Measures of social graph (posts, likes, comments)
• Measures of social text (positive, negative or neutral sentiment expressions
in posts and comments).
31

32. Study #4
Exploratory Analysis: Fuzzy Sentiment Analysis
32

33. 33

34. Study #5
Predictive Analytics: iPhone: sales and tweets
34

35. BREAK
35

36. Part IV:
Tools
“Show me the demos”
36

37. Tool #1
Social Data Analytics Tool (SODATO)
37

38. SODATO: Introduction

Purpose is to analyse social data systematically

Designed for research projects

Technical architecture and technological infrastructure





Multiple simultaneous data fetch requests (multi threading)
Self-aware of the status of fetch requests and will trigger data
aggregation and the metric engine accordingly
Console based Fetch
Aggregation and Data Export to handle large amounts of data
Email notifications
38

39. SODATO: History

From SOGATO, 2011
Hussain, A., & Vatrapu, R. SOGATO: A Social Graph Analytics Tool . Demo at the 12th European
Conference on Computer Supported Cooperative Work (ECSCW), 2011.

To SODATO, 2013
Hussain, A., Vatrapu, R., Hardt, D., & Jaffari, Z. (in press/2014). Social Data Analytics Tool (SODATO):
Description and Application to Danish Elections 2011. in Gibson, R et al. (eds). Digital Methods for Politics.
39

40. SOGATO: Old Architecture
40

41. SODATO: Current Architecture
41

42. ”Show me the demo!”
http://tinyurl.com/sodato
Username: SMWeek
Password: SMWeek
42

43. Tool #2
Danish Sentiment Analysis
http://tinyurl.com/danishsentiment
43

44. In your opinion, this text is:
Hvor er retfærdigheden for dette smukke dyr? Disse syge mennesker skulle
myrdes på samme måde, som Marius blev
33%
Ne
ut
ra
l
33%
Ne
ga
tiv
e
33%
Po
sit
iv
e
A. Positive
B. Negative
C. Neutral
44

45. In your opinion, this text is:
Okay det er i orden at gå ovenpå en død hval, men så snart en zoo skærer en giraf op
og giver til havens dyr, så flipper dk ud
33%
Ne
ut
ra
l
33%
Ne
ga
tiv
e
33%
Po
sit
iv
e
A. Positive
B. Negative
C. Neutral
45

46. In your opinion, this text is:
Giraf skal aflives Her er Marius, der til daglig bor i Zoologisk Have. Han fejler ikke
noget. Men han skal alligevel aflives på søndag.
33%
Ne
ut
ra
l
33%
Ne
ga
tiv
e
33%
Po
sit
iv
e
A. Positive
B. Negative
C. Neutral
46

47. In your opinion, this text is:
Torsdagens russiske aviser revser deres egne ishockeymænd, efter at de led et
smerteligt nederlag på 1-3 til deres finske naboer I kvartfinalen ved OL-turneringen.
'Skamfuldt for en verdensmagt',mente Sovietsky Sport, 'Brændt i finsk sauna', skrev
Kommersant, mens avisen Izvestia bedyrede, at 'Rusland ikke viste sit fulde
potentiale'.
A. Positive
B. Negative
C. Neutral
0%
Ne
ut
ra
l
0%
Ne
ga
tiv
e
Po
sit
iv
e
0%
47

48. Tool #3
Visual Analytics: #Marius
48

49. #Marius: Timeline
http://tinyurl.com/mariustimeline
49

50. #Marius: Tableau Visualizations
http://tinyurl.com/mariusvisual
50

51. Part V: CBS & You
“freemium” Engaged Scholarship Model
51

52. NETWORKED BUSINESS PROJECT
2014-1017
 Survey of Perceptions, Practices, and
Value Generation with Social Media,
Mobile, and Cloud Computing
 2729 Respondents/Orgnaizations
 Private: 2432
 Public: 297
52

53. Software: Social Data Analytics Tool
53

54. Workshop: Danish Sentiment Analysis
54

55. ABID HUSSAIN: PHD PROJECT: SOCIAL DATA ANALYTICS
55

56. CHRIS ZIMMERMAN: INDUSTRIAL PHD PROJECT: SOCIAL BUSINESS INTELLIGENCE
56

57. KATRINE KUNST: TDC PHD PROJECT: SOCIAL CONSUMPTION
57

58. SOLEY RASMUSSEN: INDUSTRIAL PHD PROJECT: NEWS-AS-A-SERVICE
58

59. ZESHAN JAFFARI: PHD PROJECT: SOCIAL BUSINESS MANAGEMENT
59

60. KOSTAS PANTAZOS: POST DOC PROJECT : INTERACTIVE DASHBOARDS
60

61. Discussion
vatrapu@cbs.dk
61