Semantic user profiling and Personalised filtering of the Twitter stream
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User Profiling on the Social
Semantic Web
Fabrizio Orlandi, DERI (NUI Galway, Ireland)
Kno.e.sis – WSU Dayton, OH – 9 Feb 2012
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User Profiling
“A user profile is a representation of information about an individual user
that is essential for the (intelligent) application we are considering” [1]
[1] S. Schiaffino, A. Amandi. 2009.
Contents of user profiles:
user interests;
the user’s knowledge, background and skills;
user behavior;
the user’s interaction preferences;
the user’s individual characteristics;
and the user’s context.
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• How to collect and interlink user information from social media
websites to build enhanced and comprehensive user profiles?
• How to manage and merge user models from different
applications and social sites in an interoperable way?
• How to leverage provenance information and trust measures on
the Web of Data to improve Web personalisation?
Research Questions
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Challenges – 1
• Information on the Social Web is stored in isolated data silos on
heterogeneous and disconnected social media websites
http://www.w3.org
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Challenges – 2
• The Web of Data: a continuously evolving “open corpus”
LOD Cloud by R. Cyganiak and
A. Jentzsch
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Challenges – 3
• Lack of provenance on the Web of Data: datasets on the Social Web
are often the result of data mashups or collaborative user activities
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Challenges – 4
• User profiles should be represented in an interoperable way in order
to exchange information across different user adaptive systems
[U. Bojārs, A. Passant, J. Breslin]
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Outline
The user profiling data process:
1. from user activities on heterogeneous social media websites,
2. to their provenance representation,
3. to the data aggregation and analysis
12
3
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So far…
State of the art analysis
Modelling the structure of wikis
Enabling semantic search on heterogeneous wiki systems
Provenance of data in wikis
Representation and extraction of provenance in Wikipedia and DBpedia
Privacy Aware and Faceted User-Profile Management
Personalized Filtering of the Twitter Stream…
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Semantic Personalization of Social
Web Streams
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Motivation
Twitter – Growth
Information Overload
11http://www.cmswire.com/cms/customer-experience/35-key-twitter-statistics-infographic-012384.php
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Motivation
• How many people should I follow?
• Am I receiving latest/complete information?
• How can I quickly tell the system what are my interests?
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Approach -- Overview
Football
Apple
User
Profiles
Filter
BroadcastThe new
iPhone has a
3.5-inch screen,
released today
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Semantic Filter
Semantic
Hub
Profile Generator
RDF
A
N
N
O
T
A
T
O
R
RDF
RSS
Store and
Query Topics
Notify Update
Fetch Updates
Get Interested
Subscribers
Create Profile
Store FOAF
The new
iPhone has a 3.5-
inch screen,
released today
Annotate: iPhone
?user foaf:interest
dbPedia:iPhone
Union
?user foaf:interest
Category:Apple
Get
Subscribers
based on
preference
Push Updates
to Interested
Users
Update RSS
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User Profiling
User Profile
Interlink social websites
Merge and model user data
Personalise users’ experience
using their profile
Integration&
User Modelling
Recommendations
Search Personalisation
Adaptive Systems
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User Profiling
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Profile Generator
• Data Extraction
– Twitter, Facebook
– Example: Tweets, FB Likes, posts, videos, etc.
• Profile Generation
– Interests extracted from collected data
• Entity spotting (user generated data)
• Explicit interests specified by user (Facebook likes etc.)
– Weighted Interests w/ DBpedia resources/categories
– FOAF profile
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Semantic Filter
Semantic
Hub
Profile Generator
RDF
A
N
N
O
T
A
T
O
R
RDF
RSS
Store and
Query Topics
Notify Update
Fetch Updates
Get Interested Subscribers
Create Profile
Store FOAFUpdate RSS
Semantic Filter
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Semantic Filter
• Twitter Storm:
– Distributed realtime computation system
• Microblog Metadata
– Twitter provides metadata
• Author, date, location etc..
– Metadata Extracted
• DBpedia Entities, URLs
• Generate SPARQL Query representing interested Users
– Retrieved at Semantic Hub
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Semantic Filter
Semantic
Hub
Profile Generator
RDF
A
N
N
O
T
A
T
O
R
RDF
RSS
Store and
Query Topics
Notify Update
Fetch Updates
Get Interested Subscribers
Create Profile
Store FOAFUpdate RSS
Semantic Hub
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Semantic Hub
• RSS Extension
– Preference – to include the SPARQL queries
• Push content
– FOAF profiles of the subscribers are matched with the
preference
– Interested subscribers receive the content
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DERI’s Unit for Social Software
(USS)
Unit leader: John Breslin
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Overview of research activities
• Research team at DERI
– Two postdocs (plus one starting on Monday)
• Alex Passant (10%), Maciej Dabrowski, Bahareh Heravi
– Nine PhD students
• Six supervised by John, two by Alex, one by Michael H
• Various interdisciplinary collaborations
– Exercise, e-government, political science, journalism
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Current students
David Crowley
• Citizen sensors
– Funded by College of
Engineering and Informatics
• Attaching data from
sensors to social web
content using semantic
technologies
Ted Vickey
• Exercise adherence via
social networks
– Funded by American Council
on Exercise and IRCSET
• Developing a classification
for fitness tweets to see if
sharing exercise regimes
can encourage others
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Current students
Antonio Aguilar (EEE)
• Heart rate variability
analysis
– Funded by Assisted Ambient
Living eCAALYX EU project
• Developing methods to
help predict sudden
cardiac death using non-
linear algorithms
Fabrizio Orlandi
• User profiling on the Social
Semantic Web
– Funded by Cisco Foundation
and IRCSET
• Consolidating user profiles
from various platforms and
deriving interests from
amalgamation
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Current students
Lukasz Porwol
• e-Participation via social
media
– Funded by Science
Foundation Ireland
• Leveraging popular
networks for e-government
instead of standalone
platforms
Owen Sacco
• Trust, accountability and
privacy via Linked Data
– Funded by Cisco Foundation
and IRCSET
• Developing privacy
preference managers for
the Semantic Web
• Collaboration with US
Government
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Current students
Marie Boran
• Connecting data journalists
with linked scientific data
– Funded by Science
Foundation Ireland
• Bridging the gap between
experimental data from
scientists and the
mainstream media
Jodi Schneider
• Argumentative discussions
– Funded by Science
Foundation Ireland
• Representing, classifying
and visualizing
argumentative discussions
on the Web
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Current students
Myriam Leggieri
• Linked sensor data
– Funded by SPITFIRE
• Connecting sensor data
with explanatory facts from
the Linked Open Data
Cloud
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Some past postgraduate students
• Sheila Kinsella
– ECE graduate, now engineer with Datahug
• Haklae Kim
– Now senior engineer with Samsung
• Uldis Bojars
– Now with the National Library of Latvia
• Gerard Cahill
– BSc IT graduate, now developer with Starlight
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DERI – House
eBusiness
Financial Services
Health Care
Life Sciences
eLearning
Green &
Sustainable ITeGovernment
Stream 3: Semantic
Information Mining
Information
Mining
and Retrieval
Natural Language
Processing
Stream 1:
Semantic Search
Reasoning and
Querying
Data Intensive
Infrastructure
Stream 2: Semantic
Collaboration
Semantic Colla-
borative Software
Social Software
Stream 4: Semantic
Middleware
Service Oriented
Architecture
Sensor
Middleware
Linked
Data
Research
Centre
DERI Applied
ResearchCommercialisation
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Some additional stats…
• On average for:
– 200 Tweets
– 200 Facebook posts, and items.
• ~106 interests - DBpedia instances
• ~720 interests - DBpedia categories (~6.8 times more)
• Estimated average Recall: 0.74
• 22 users
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