You tube Group Profiling Services

ImREAL WP4 Augmenting User Models YouTube Services Deriving Social Group Profiles from YouTube for Learner Modelling Presented By: Ahmad Ammari User and Community Modelling School of Computing, University of Leeds, UK [email_address]

Learner Modelling for Learning Simulators Simulated Environment Existing Learner Model
Simulated learning environments should adapt to meet the learner need
This is achieved by building a learner model that include information about the learner, such as her demographic information, as well as her awareness, interests, knowledge, and her learning needs
However, the problem that simulated learning environments face is that the information about a new learner is very limited because it can only come from the very initial interaction between the learner and the simulator, leading to limited a user profile
This means that the existing user model has a limited scope which does not help simulators to well adapt to the learner’s needs

Socially Enriched Learner Model Socially-Enriched Learner Modelling
A solution to this problem is to go beyond the interaction between the learner and the simulator, and start looking at the digital traces that people create in other parts of the world.
Open social spaces can be one of the resources to derive these digital traces.
The learners, or users who are similar to the learners in certain aspects, can be very active in creating content about themselves on the social web.
Mining this content can create rich social profiles, which allow to augment our limited user model, creating a socially enriched learner model.

YouTube Services
Address the Problem of Limited Learner Models
Suite of Services that mine User-Created Content Retrieved from the Video Sharing Site YouTube ( User Comments on Videos )
Derive Socially-Enriched Group Profiles from Relevant comments on YouTube Videos
3 2 1

Methodology Semantically Enriched Machine Learning Framework Social Media Source: YouTube www.youtube.com Social Content: Public Comments on Uploaded Videos Activity Domain: Job Interviews Objective: Derive Profiles of YouTube User Groups that can help in: 1. Identification of Learning Needs 2. Augmenting Learner Models

Methodology … … Noise Relevant 1 2 3

Methodology (cont.) Cluster– based Group Profiles P1 P2 … Relevant Comments 4 5A

Methodology (cont.) … Relevant Comments 4 5B Demographics – based Group Profiles

Deriving the Individual & Group User Profiles TFIDF Weights Of Domain Concepts Demographic Features retrieved from YouTube User Profiles Text Clustering Statistical Destribution C1 C2 C3 … C n Age Gender Location 0.3 0.6 0.9 … 0.4 30 M US 0.8 0.2 0.3 … 0.6 25 M GB 0.4 0.5 0.2 … 0.1 15 F IN

Pilot Experiment 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

Example Usage 1 Using the Cluster-based Group Profiles to Identify Learning Needs for Similar Learners URL for all Derived Cluster-based Group Profiles: http://wis.ewi.tudelft.nl/imreal/u-sem/YouTubeServices/YouTubeGroupProfiles_files/Clustering_Based_Groups.html

Example: Body Language Signals

Example Usage 2 Using the Demographics-based Group Profiles to Augment Models of Similar Learners URL for Example Demographics-based Group Profiles to augment Models of Four Fictitious Learners: http://wis.ewi.tudelft.nl/imreal/u-sem/YouTubeServices/YouTubeGroupProfiles_files/Demographic_Based_Groups.html

Example (1): Body Language Signals
Which Body Language Signals each Learner is aware of?
The frequent body language signal that adults in GB talk about is the “eye contact”
However, we do not inspect eye contact as a key body language signal for US adults.
On the other hand, other body parts used in body language signals, such as “fingers” and “hands”, frequently exist in the comments written by US users
Extracted concepts from content written by users in Asia do not suggest that they are interested in or aware of body language signals.

Example (2): Expressed Emotions
Which Emotions each Learner expresses?
US adult job candidates are more inclined to express their anxious emotional states when talking about job interviews. This can be sensed from the “nervous” concept being only in the group of US adults
GB adults on the other hand tend to show more confidence by using terms like “hope” and “helpful”
Asian adults did not express job interview related emotions

Example (3) : Learner Interests
What is each Learner interested in?
Asian users show more interests in watching interview and job hunting guides than users in US and GB.
Interest by users in Asia and US in the financial aspect can also be seen in relevant terms such as “money” (Asia, US) and “pay” (Asia).
US users show more interests in “companies” and “education” in addition to money.
Both GB and US users tend to mention the “interviewer” more frequently than mentioning the interviewee, as opposite to users in Asia who tend to mention the “candidate” more frequently.

YouTube Services Webpage: http://wis.ewi.tudelft.nl/imreal/u-sem/YouTubeServices/ ImREAL Project: http://imreal-project.eu/ Presented By: Ahmad Ammari User and Community Modelling School of Computing, University of Leeds, UK [email_address] Thank You

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You tube Group Profiling Services

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