This document proposes an architecture for personalized filtering of Twitter streams. It aims to reduce information overload by only delivering tweets relevant to a user's interests, as determined by their social media profiles. The architecture uses semantic web technologies to annotate tweets with concepts, generate user profiles representing interests, and filter the tweet stream using SPARQL queries over these profiles. A semantic hub then pushes only the matching tweets to interested subscribers. The system was presented as a full paper at ISWC 2011 and aims to address limitations of isolated social media data silos through interlinked user profiles and interest-based dissemination of updates.

1. Personalized Filtering of
the Twitter Stream
Pavan Kapanipathi 1,2, Fabrizio Orlandi1,
Amit Sheth2 ,Alexandre Passant 1
1 Digital Enterprise Research Institute, Galway – Ireland
2 Kno.e.sis, Dayton, OH- USA
1

2. Motivation
Twitter – Growth
Information Overload
http://www.cmswire.com/cms/customer-experience/35-key-twitter-statistics-infographic-012384.php
2

3. Motivation
• How many people should I follow ?
• Am I receiving latest/complete information ?
3

4. Background
 Twarql – Streaming annotated tweets
 Semantic Web Technologies
 Annotate Tweets (DBpedia Entities)
 Filter Stream using SPARQL Queries formulated
 Example:
 Stream all the tweets related to Semantic Web generated in
Germany
?tweet moat:taggedWith ?topic .
?topic dcterms:subject category:Semantic_Web .
?tweet sioc:has_creator ?user .
?user geonames:locatedIn dbpedia:Germany .
4

5. Approach -- Overview
The new
iPhone has a Broadcast
3.5-inch screen,
Football
released today
User
Profiles
Filter
Apple
5

6. Annotate: iPhone Get
?user foaf:interest Subscribers
The new
iPhone has a 3.5-
inch screen,
Architecture
dbPedia:iPhone
Union
based on
preference
?user foaf:interest
released today
Category:Apple
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Push Updates
to Interested
Users
Create Profile
6

7. Contribution
 Profile Generator
 Automatic generation of User Profiles
 Semantic Filter
 Annotating Twitter Stream with concepts from Linked
Open Data
 Semantic Hub
 Delivering tweets to appropriate Interested Users (near
real-time)
7

8. Profile Generator
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
8

9. Profile Generator
Disconnected
Social websites
Isolated data
silos
Social Networking Sites as Walled Gardens by David Simonds (Used with permission)
9

10. Interlink social websites
Integration
& Merge and model user data
User Modelling
User Profile
Personalise users’ experience
using their profile
Recommendations Adaptive Systems
Search Personalisation
10

11. Profile Generator
 Data Extraction
 Twitter, Facebook, LinkedIn
 Example: Tweets, FB Likes
 Profile Generation
 Interests extracted from collected data
 Entity spotting (user generated data)
 Explicit interests specified by user (Facebook likes etc)
 Weighted Interests
 Semantic Representation of Profiles
 FOAF profile
11

12. Semantic Filter
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
12

13. Semantic Filter
 Twitter Streaming API
 Microblog Metadata
 Twitter provides metadata
 Author, date, location etc..
 Metadata Extracted
 DBPedia Entities, URLs
 Generate SPARQL Query representing interested Users
 Retrieved at Semantic Hub
13

14. Semantic Filter – RDF
<http://twitter.com/rob/statuses/123456789>
rdf:type sioct:MicroblogPost ;
sioc:content "P Groth and Y Gil, Linked Data for Network Science
http://bit.ly/owxcJg #iswc2011 #lisc2011 #linkeddata-“•
sioc:has_creator <http://twitter.com/rob> ;
foaf:maker <http://example.org/rob> ;
moat:taggedWith dbpedia:Linked_Data ;
moat:taggedWith dbpedia:Network_Science ;
<http://twitter.com/rob/statuses/123456789#presence>
rdf:type opo:OnlinePresence ;
opo:startTime •2010-03-20T17:55:42+00:00 ;
opo:customMessage <http://twitter.com/rob/statuses/123456789> .
<http://twitter.com/rob> geonames:locatedIn Dbpedia:Ohio .
[...]
14

15. Semantic Filter– SPARQL Query
 Generate SPARQL Queries
 Representing FOAF of interested users
SELECT ?user WHERE {
{ ?user foaf:interest dbpedia:Linked_Data .}
UNION
{ ?user foaf:interest dbpedia:Network_Science .}
}
15

16. Semantic Hub
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
16

17. PubSubHubbub
 Protocol
 PubSubHubbub is an extension to RSS/Atom
 Open, web hook based, pubsub protocol for Real-time notification
of updates
 Drawback
 Publisher has no control over the dissemination of his content
 Extension – Semantic Hub
 Publisher controlled dissemination
 SPARQL Query representing the subset of target subscribers
17

18. PubSubHubbub Protocol
Extension
Hey I have new Here is the
Give me new content
content for feed the new
X + my of feed X
content Sub - A
preference Y
Sub - B
Pub Semantic Hub
Sub - C
Here it Sub - D
is
Get the subscribers
Social of Pub whose profile
Graph matches preference
Y
18

19. 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
 Accepted as a full paper in the In-Use track at ISWC 2011
19

20. Conclusion
 Single consistent profile rather than profiles on multiple social networks
 User Profile Generation
 Architecture for Personalization of twitter stream
 Reduce load on users to follow others
 Public tweets streamed
 Access to information from experts in domains
 Are you following experts in your domain of interest?
 Experts public tweets will be streamed
 Dynamic groups of users
 Interest Driven
20

21. Future work -- Why RDF
 Twarql features
 Concept feeds as interests of the users

22. Future Work
 Periodic FOAF profile generation for users
 Twitter Stream reflecting the changing interests
 Extending to other social networks (G+, FB)
22

23. Thanks
Contact us on Twitter 
@pavankaps
@badmotorf
@terraces
@amit_p
Email: {pavan, amit}@knoesis.org
{fabrizio.orlandi, alexandre.passant}@deri.org
This work is funded by (1) Science Foundation Ireland under grant number SFI/08/CE/I1380 (Lıon 2) and by an
IRCSET scholarship supported by Cisco Systems (2) Social Media Enhanced Organizational Sensemaking in
Emergency Response, National Science Foundation under award IIS-1111182, 09/01/2011 - 08/31/2014.
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24. 24

25. Architecture
25

26. Agenda
 Motivation
 Contribution
 Architecture
 Conclusion
 Future Work
26

27.  Weighing function based on RTs and other active
engagements of the user
27