1. The presentation discusses deriving group profiles from social media data to help design simulated learning environments. 2. An experimental study combined semantics and machine learning to profile groups based on their digital traces from a job interview domain. 3. Preliminary results found the group profiles could help training professionals identify learning needs, and domain concepts could augment learner models. However, improving profile quality and demographic data accuracy requires further work.

1. Deriving Group Profiles from Social Media to
Facilitate the Design of Simulated Environments
for Learning
Ahmad Ammari, Lydia Lau, Vania Dimitrova
The University of Leeds, UK
at
Learning Analytics and Knowledge 2012, Vancouver, Canada
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2. In this presentation …
• Vision of ImREAL as motivation
• Potential of semantics in smart social
spaces for learning applications
• Experimental study on combining
semantics and machine learning for group
profiling of digital traces
• Lessons learned
• Future challenges
2

3. Immersive Reflective Experience
based Adaptive Learning Vision
In a simulator for
learning
Forethought Reflection
In the real world
3

4. Consortium (2010-13)
University of Leeds, UK
- Project Coordinator/Scientific Coordinator
Trinity College Dublin, Ireland
Graz University of Technology, Austria
University of Erlangen-Nuremberg, Germany
Delft University of Technology, The
Netherlands
Imaginary Srl, Italy
EmpowerTheUser Ltd, Ireland
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5. Smart Social Spaces
– semantic underpinning
Sensors
& collectors
Noise
filtration Semantic
augmentation
Group Ontologies
service
profiling
Viewpoint Semantic
Semantic query
Semantic service
service
data browsers
Smart social spaces
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6. This talk …
1. Sensors
& collectors
2. Noise
filtration + supervised
Ontologies machine learning
3. Group + unsupervised
profiling machine learning
Smart social spaces
Interpersonal skills for
Job interview 6

7. Noise Filtration Service
• Input: social media content (e.g. YouTube
comments)
• Filters the noise from social media content by
removing the content that are not useful to
generate social profiles
• Output: clean social media content, author
IDs
Support service to social profiling services.
Clean content reflects awareness of
authors in domain aspects (e.g. Job Interview7

8. The Social Noise Filtration Service:
Methodology
Semantically Enriched
Experimentally Bag of Words (BoW)
Controlled Ground Truth Corpus
Analyze
Comments
SCORE
Term – Comment
Matrix
(Training Corpus)
S
C
Public Pre- O
R
Comments Process E
On YouTube S 8

9. Example Comments
Comment score
I think trying to decipher gestures as to have a general 8.0
meaning is a bit too vague. You have to put the
background, education, personality, and the culture of
the individual into consideration. Gestures are often
misunderstood and not the clearest form of
communication. For example…
…I will comment that most of us have grown up with 7.7
being told that strong eye contact (without looking
psychotic) is good … However, I agree that you notice if
someone is not used to it and seems intimidated. At this
point it is a good to look away periodically.
Interview on Wednesday, hope it goes well 0.68
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10. Group Profiling
… …
Relevant Noise
P1
Clustering
– based
Group
P2 Profiles
Demographic
– based
Group
Profiles
Adult Female
10
USA UK

11. Exploration experiment
Purpose is to answer the following:
Q1: Can we generate useful group profiles
to aid training professionals in identifying
learning needs?
Q2: Can we derive learning domain
concepts to augment learner models?
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12. Dataset used
Data Property Value
Number of Job Interview-related YouTube Videos 17
Number of Comments Retrieved 1465
Number of Remaining Comments after Noise Filtration 471 (32%)
Number of Unique Comment Authors 393
Comment to Author Ratio 1.20
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13. Sample Output
Clustering–based Group Profiles
Third largest group – Size: 36 Authors, 9% of
population
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14. Sample Output
Demographic–based Group Profiles
Location: GB – Age: From 20 To 40 years
Frequent Job
Interview_good, eye_contact,
Interview
eyes, interviewer, hope, helpful
Concepts
Location: US – Age: From 20 To 40 years
Frequent Job Good_Interview, people, company, interviewer,
Interview time, girl, experience, answer, money,
Concepts questions, nervous, education, fingers, hands
Location: Asia– Age: From 20 To 40 years
Frequent Job questions, answers, candidate,
Interview interview_guide, money, pay, job_guide, watch
Concepts
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15. Lessons Learned
• On noise filtration
– Choice of threshold for noise filtration?
– What is “inappropriate” content?
– Can “promotional” content be detected?
• On potential of group profiles to aid
training professionals and learner model
augmentation
– Authentic comments were liked
– Would be useful to know more about the
viewpoints within a group
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16. Future work
• Increase use of semantics (e.g. For
viewpoints extraction)
• Improve quality of group profiling (e.g. By
understanding the impact of clusters
sorted by age)
• How to get more accurate demographic
data (e.g. „Place‟ from YouTube was not
reliable)
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17. Deriving Group Profiles from Social Media to
Facilitate the Design of Simulated Environments
for Learning
http://www.imreal-project.eu/
Ahmad Ammari, Lydia Lau, Vania Dimitrova
The University of Leeds, UK
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