Nowadays people share everything on online social networks, from daily life stories to the latest local and global news and events. Many researchers have exploited this as a source for understanding the user behaviour and profile in various settings. In this paper, we address the specific problem of user behavioural profiling in the context of cultural and artistic events. We propose a specific analysis pipeline that aims at examining the profile of online users, based on the textual content they published online. The pipeline covers the following aspects: data extraction and enrichment, topic modeling, user clustering, prediction of interest, content analysis including profiling of images and subjects. We show our approach at work for the monitoring of participation to a large-scale artistic installation that collected more than 1.5 million visitors in just two weeks (namely The Floating Piers, by Christo and Jeanne-Claude). We report our findings and discuss the pros and cons of the work.

1. Analysis of Online User Behaviour
for Art and Culture Events
Marco Brambilla Tahereh Arabghalizi Behnam Rahdari
Marco Brambilla
Contacts: @marcobrambi, marco.brambilla@polimi.it, http://datascience.deib.polimi.it
UNIVERSITY OF
PITTSBURGH

2. Agenda
Context
Method
• Pre-processing
• Analysis: topics, user clustering, images
• Prediction of interests
Challenges & Conclusions

3. Context
• Role of social media in our life
• Social media for cultural and artistic events
• Behaviour and content
• Multi-disciplinary collaboration on social media analysis and
cultural heritage
• Collaboration: Politecnico di Milano, Musei di Brescia, University
of Pittsburg

4. Research Questions
Topics of interest of visitors?
Categorization of users?
Demographics of visitors?
Engagement and online
participation?
Relation between photos, time,
location, text and the event?

5. Approach
Domain-specific pipeline to profile social media users
and content in cultural or art events

6. Case Study
The Floating Piers by Christo and Jeanne Claude
Iseo Lake, Italy
June 2016

7. Case Study

10. Case Study
• 17 MLN $
• 220,000 floating blocks
• 1.5 MLN visitors in 16 days

11. Pre-processing

12. Data Extraction
• Using Instagram and Twitter APIs
• Extract relevant tweets/posts during the event
• Extract all relevant users
o That tweet/post directly
o that like, comment, retweet, etc.
• Extract all properties
o Textual: bio, tweet/post text, hashtag, etc.
o Quantitative: #followers, #followings, etc.
o Media: photos, metadata (geotag, …)

13. Tweets Posts
14,062 30,256
Users Users
23,916 94,666
Authors Reacting Authors Reacting
7,724 16,197 16,681 77,985
From June 10th to July 30th
Collected Data

14. • Text normalization (NLP)
• Language identification and translation
• Gender detection
• Data cleansing
• Store clean and transformed data
Preprocessing

15. Time Distribution (Twitter)

16. Time series – Instagram vs. Twitter

17. Instagram Likes and Comments

18. Italy Lombardy Region Iseo Lake
Geographical Distribution (Instagram)

19. Data Analysis Process
1. Document Term Matrix (DTM)
2. Topic Extraction
3. Dimension Reduction
4. Cluster Analysis and Validation
5. Prediction
6. Media Analysis
7. Content Network Analysis

20. Topics

21. Document-term Matrix
A matrix that describes the frequency of terms that
occur in a collection of documents
Terms
Documents
Art Travel Italy Design …
Post 1 0 1 1 0
Post 2 1 2 0 1
Post 3 0 0 1 0
Post 4 1 1 3 1
…

22. Topic Extraction
Latent Dirichlet Allocation (LDA):
documents as mixtures of topics (with probability)
Input: Document Term Matrix
Outputs: Topics, Topic Probabilities Matrix
Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 …
Post 1 0.19 0.16 0.27 0.14 0.11 0.13
Post 2 0.31 0.18 0.21 0.08 0.10 0.12
Post 3 0.25 0.24 0.20 0.17 0.09 0.05
Post 4 0.19 0.32 0.22 0.10 0.07 0.10
…

23. Dimensionality Reduction
• Too many topics extracted with LDA
• Using Principle Component Analysis (PCA) to extract a smaller set
of linearly uncorrelated topics
> 0.95
Variance share Cumulative variance share

24. User Clustering

25. Cluster Analysis
• Apply clustering algorithms over Topic Probabilities
Matrix to cluster users
• Multiple data slices
• Multiple algorithms
o K-means
o Hierarchical
o DBSCAN
Topic 1
Topic 3
Topic 2

26. Cluster Validity
• How to evaluate the “goodness” of the resulting
clusters?
• Validation Measures
– Internal : ex. Silhouette Coefficient, Dunn’s Index,
Calinski-Harabasz index, etc.
– External: ex. Entropy, Purity, Rand index, etc.

27. User Clustering

28. Travel
Lovers
Art
Lovers
Internet & Tech
Lovers
Users’ Biography Word Clouds
Cluster Labeling

29. Word Network for Clusters

30. Travel Lovers
Art Lovers
Tech Lovers
Hierarchical Clustering

31. Language
Gender
Impact of Demographics

32. Prediction

33. Prediction
Predict the category or the interest area of potential new users for
similar cultural or art events in the future
Decision Trees
o Prepare Required Data
o Grow Decision Tree
o Extract rules from the tree
o Predict using test data
o Evaluate

34. Extracted Rules
Rule 1 : if (0.36 < Bio_score < 0.37 OR Bio_score < 0.35)
then Travel Lover
Rule 2: if (0.35 < Bio_score < 0.36 AND Status_count >
14.5) OR (Bio_score > 0.37 AND language != Italian)
then Art Lover
Rule 3: if (Bio_score > 0.37 AND Language = Italian) then
Tech Lover
Otherwise: Not Interested
accuracy = 62 %
Prediction rules

35. Decision Tree

36. Image Analysis

37. People in Pictures

38. Age Sex
50.4% female
49.6% male
Visitor Analytics
Race
Bias of the medium?

39. Objects in Pictures Hashtags
Users tend not to report the actual content of the photos
in their textual descriptions /hashtags
Object Extraction from Pictures

40. Main color shades among all photos
Color Detection
for Subject Identification

41. Confusion Matrix
Simple techniques “good enough”?
Objects or Colors?

42. Ongoing Challenges

43. Future Challenges of KE
Determining exact
positioning based on
perspective

44. Future Challenges of KE
Network structures
and their temporal
evolution
Max graph perturbation
Daily graph variations

45. Future Challenges
Real cross-disciplinarity
(cultural heritage, humanities,
social science)
No one at the cultural part of the
event!
Exhibit--->

46. Conclusions
• (Sometimes) Simple methods work just fine
• Interesting profiling and behaviour detection
• Still far from cross-disciplinary approaches
• How much: human vs. machine?
• Data Visualization and Crowdsourcing

47. Contacts: Marco Brambilla, @marcobrambi, marco.brambilla@polimi.it
http://datascience.deib.polimi.it
http://www.marco-brambilla.com
Analysis of Online User Behaviour
for Art and Culture Events
Marco Brambilla, Tahereh Arabghalizi, Behnam Rahdari