We study how users of multiple online social net- works (OSNs) employ and share information by studying a common user pool that use six OSNs – Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users’ sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.

1. Web Information Retrieval / Natural Language Processing Group
1
#mytweet via Instagram:
Exploring User Behaviour Across
Multiple Social Networks
Bang Hui Lim, Dongyuan Lu
Tao Chen, Min-yen Kan

2. 2
Background
Introduction
1. http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
2. http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/
3. http://www.globalwebindex.net/blog/internet-users-have-average-of-5-social-media-accounts
Numberofusers/million
0
400
800
1200
1600
Active users worldwide 1
• 74% of Internet users use
Online Social Networks
(OSN)2
• Average user has 5.54 Social
Media accounts3
• Uses 2.82 sites actively

3. 3
• Most research done on single OSN
• Holistic modelling of users
• Multi OSN research:
• Forecasting evolution of trending topics across different
OSNs (Althoff et al. 2013)
• Internetwork interactions (Chen et al. 2014)
• How users behave across Image-based and Text-based
networks (Ottoni et al. 2014)
Motivation
Introduction

4. 4
• 6 OSNs: Flickr, Google+, Instagram, Tumblr, Twitter and
Youtube
• Multi-network analysis of user behaviour
• Cross network interactions
• Publicly available data
• No image / video analysis
Scope
Introduction

5. 5
3 ways to cross-share
1. Native feature
Instagram
2. Third party app
Hootsuite
3. Copy and paste
Twitter to Facebook
Introduction

6. 6
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

7. 7
Profile
Description
OSN
accounts
Dataset - https://about.me

8. 8
• 6 OSNs
• 2011 - 2014
• 32 K Users
• 4 accounts / user
Dataset
Dataset
Flickr Google+ Instagram Tumblr Twitter Youtube
11.9K
27.8K
16.7K
20.4K19.6K
14.5K
Users
Flickr Google+ Instagram Tumblr Twitter Youtube
1.3M
88.4M
9M2.8M2.6M14.8M
Posts

9. 9
User Statistics
Twitter Google Instagra Tumblr Flickr YouTube
Twitter 79.4 76.4 65.2 64.4 56.2
Google 96.4 73.5 61.7 61.0 65.0
Instag 96.7 76.8 68.5 60.4 51.0
Tumblr 96.0 74.9 78.8 59.4 49.2
Flickr 96.0 74.8 71.0 60.1 53.3
YouTub 95.5 84.1 68.4 56.6 60.9
also use:
%ofuserswhouse:
Dataset

10. 10
• 40% of users are active on any given day
• More networks utilised on days with higher activity
Activity Statistics
Dataset
Average number of networks used, daily
(2, 0.9) (2.75, 0.9)

11. 11
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

12. 12User Profile
👤
I’m a Digital Media Specialist passionate
about self education, lifelong learning...
Explore Dream Create.
Knowledge is freedom. I run a website called DIY
Genius that helps young people self education.
All my photographs are posted under the creative
commons non commercial attribution...
I’m interested in digital media, adventure
sports, and mountains.
A collection of videos I’ve filmed on my iPhone
while hiking skiing and biking in the mountains.

13. 13
• Pairwise Jaccard Coefficient
Self-description Similarity
User Profile

14. 14
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

15. 15
• Multicasting user activity over multiple social networks.
• Source-sink relationship between OSNs
Cross-sharing
Posts - Cross-sharing
Source
Sink

16. 16
Source - Sink Graph
Posts - Cross-sharing

17. 17
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

18. 18
• Different peaks for activity
levels on weekend and on
weekdays
Time of Day
Posts - Temporal Analysis
Weekday
Weekend

19. 19
» Different uses for OSNs - personal vs work
Day of Week
Posts - Temporal Analysis

20. 20
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

21. 21
The “Average user”
Posts - Topical Analysis

22. 22
• User description keywords
• 3 professions: Developer, Producer, Marketing expert
• How do different professions use different OSNs?
• OSN for work, OSN for personal use
Meso-Level Groups: Profession
Posts - Topical Analysis

23. 23
Topic Modeling
Topic 1
• love
• father
• mother
• vacation
• dinner
Topic 1
• love
• father
• mother
• vacation
• dinner
LDA
1. Family
2. Technology
3. Music
4. <Unknown>
.
.
.
50. Food
Collection of Documents
(posts)
Top words that
belong to a cluster Inferred topics
Topic 1
• love
• father
• mother
• vacation
• dinner
• brother
• […]
• Latent Dirichlet Allocation (LDA)(Blei et al., 2003)
Posts - Topical Analysis

24. 24
Matching
Developer
Technology
.
.
Mobile
Gadgets
.
.
Food
TopicsProfession
👤
“New Ubuntu image
available on Digital
Ocean. Sweet!”
“I love taco bell :D”
Posts
LDA
• Match topics to professions manually
Posts - Topical Analysis
Work related
Non-work related

25. 25
Workaholic: Top 2 frequently topics are work related
Many People are Workaholics!
65%
70%
75%
80%
85%
90%
Producer Marketing Expert Developer
UserPercentage
Posts - Topical Analysis

26. 26
OSNs for Work Related Posts
UserPercentage
0%
10%
20%
30%
40%
Twitter Google+ Youtube Tumblr Instagram Flickr
Producer Marketing Expert Developer
Posts - Topical Analysis

27. 27
• Introduction
• Dataset
• User Profile
• Posts
- Cross-sharing
- Temporal Analysis
- Topic Analysis
• Conclusion
Outline

28. 28
• 6 OSNs: Flickr, Google+, Instagram, Tumblr, Twitter and
Youtube
• Most users describe themselves differently.
• OSN cross-sharing directionality - sink and source
• YouTube and Instagram are popular content originators
• Twitter is a content aggregator
• OSNs for work and personal use
• Dataset will be available at: http://wing.comp.nus.edu.sg/
downloads/aboutme
Conclusion
Conclusion

29. 29
👤
I’m a Digital Media Specialist passionate
about self education, lifelong learning...
Explore Dream Create.
Knowledge is freedom. I run a website called DIY
Genius that helps young people self education.
All my photographs are posted under the creative
commons non commercial attribution...
I’m interested in digital media, adventure
sports, and mountains.
A collection of videos I’ve filmed on my iPhone
while hiking skiing and biking in the mountains.
Conclusion

30. Web Information Retrieval / Natural Language Processing Group
30
Thank you!