Stumbl: Using Facebook to Collect Rich Datasets for Opportunistic Networking Research
15
Thrasyvoulos Spyropoulos EURECOM, France Stumbl: Using Facebook to Collect Rich Datasets for Opportunistic Networking Research Theus Hossmann ETH Zürich, Switzerland Franck Legendre ETH Zürich, Switzerland
-
Upload
theus-hossmann -
Category
Documents
-
view
848 -
download
3
description
The Fifth IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications 2011, Lucca, Italy
Transcript of Stumbl: Using Facebook to Collect Rich Datasets for Opportunistic Networking Research
Thrasyvoulos Spyropoulos
EURECOM, France
Stumbl: Using Facebook to Collect Rich Datasets for Opportunistic Networking
ResearchTheus Hossmann
ETH Zürich, Switzerland
Franck Legendre ETH Zürich, Switzerland
Motivation
• Opportunistic Networks• Natural disasters (floods, earthquakes, etc.) • Political censorship (Egypt, ...) • Rural regions• Offloading infrastructure
• Traditional random protocols (e.g., epidemic routing) are inefficient!
• Patterns in human behavior help to improve efficiency
Need to understand different aspects of human behaviorNeed to understand different aspects of human behavior
Motivation
Mobility
Communi-cation
Social
?
Who meets whom?☞ Forwarding opportunity
Who knows whom?☞ Trust, altruism Who communicates
with whom?☞ Forwarding necessity
How do mobility, social and communication relate to each other
How do mobility, social and communication relate to each other
Motivation
Mobility
Communi-cation
Social
?
Why? Difficult and expensive to make mesaurements in large scale!
Why? Difficult and expensive to make mesaurements in large scale!
Existing studies do not capture all dimensions and/or are limited in size
Existing studies do not capture all dimensions and/or are limited in size
Mobility trace analyses
[Tuduce 2006]
[Chaintreau 2007]
[and many more]
Mobility + Social
[Mtibaa 2008]
[Eagle 2009]
[Scellato 2010] Mobility + Social +
Comm.
[Pietiläinen 2009]
• FB users report whom of their friends they meet• Initialization phase
• Users choose a subset of their FB friends• Max 20 FB friends as Stumbl friends• Classified as: friend, family, colleague, acquaintance
• Reporting phase• Users report their face-to-face meetings every day• number of meetings, aggregate meeting time,
contexts of the meetings (work, fun, home, meal, other)
• Collecting interaction• Wall posts (messages, photos, videos, links)• Comments on wall posts• Likes
Stumbl Facebook Application
http://apps.facebook.com/_stumbl/
http://apps.facebook.com/_stumbl/
Stumbl Experiment
• 39 Participants personally invitation (colleagues + friends)
• 3 weeks (August – September 2010)
• Incentives: Raffle• One winner after every week• Drawn at random with probability depending on
• # of days participated• # of friends participating
Dataset
• On avg. 22 (of 39) users reported data per day
• On avg. a user selected 14 Stumbl friends• 11 users selected the maximum allowed 20 friends• “most social” user reported meetings with 17 friends
• 498 Facebook pairs• 47 with mutual meeting reports
• Validation of user reports• 86% of days, the reports of having a meeting match
Dataset
• Number of unique friends met• Percentage of days a pair meets• Number of meetings per context
Mob.
Soc. Com.
?
Less than 10 Facebook friends met during the 3 weeks on average
Less than 10 Facebook friends met during the 3 weeks on average
~5% of pairs meet every day~5% of pairs meet every day
Most meetings happen at workMost meetings happen at work
Dataset
• Number of pairs per social tie type
• Facebook communication events
Mob.
Soc. Com.
?
Most Facebook friends you meet are friends (>60%)Most Facebook friends you meet are friends (>60%)
Posts Comments Likes Total
199 341 103 643
Results
• How long and often pairs meet per day,depending on social tie type?
Mob.
Soc. Com.
?
The social tie type has very strong impact on meeting characteristics in terms of context, duration and
frequency of meetings ☞ Tie type is valueable data for routing
The social tie type has very strong impact on meeting characteristics in terms of context, duration and
frequency of meetings ☞ Tie type is valueable data for routing
Results
• How do your social relationship types influence communications?
Mob.
Soc. Com.
?
Friends and family are the most communicative☞ Tie types should be considered in traffic modeling
Friends and family are the most communicative☞ Tie types should be considered in traffic modeling
Results
• Are we more or less likely to communicate with Stumbl vs. FB friends?
Mob.
Soc. Com.
?
A Facebook user communicates 10x more with Stumbl friends
☞ Communication ties are more local than social ties
A Facebook user communicates 10x more with Stumbl friends
☞ Communication ties are more local than social ties
Outlook
• Extended analysis• Multi-relational graph analysis• Structure (communities, clustering, path lengths, etc.)?• Nodes (centrality rankings, etc.)?
• Sharing the data• Anonymization
• Bigger experiments! Incentives??• Incentives must not motivate false reports• Game mechanism?
Please
HelpPlease
Help
Take Home Messages
• Stumbl uses Facebook to collect data about• Communication• Social Ties• Mobility
• Potential scalable way to collect data from large numbers of users!
• Preliminary experiment and analysis shows that• The social tie type has very strong impact on meeting
characteristics in terms of context, duration and frequency of meetings
• The social tie type has strong impact on how communicative a pair is
• Communication ties are more local than social ties
http://apps.facebook.com/_stumbl/http://apps.facebook.com/_stumbl/
![Opportunistic IoT: Exploring the Harmonious Interaction ...guob.org/research/Opportunistic-IoT-JCNA.pdf · the development of opportunistic networks [3], which uses infrastructure-free,](https://static.fdocuments.net/doc/165x107/5fb9b8e92567ec340653523e/opportunistic-iot-exploring-the-harmonious-interaction-guoborgresearchopportunistic-iot-jcnapdf.jpg)
