Personalized Filtering of Twitter Stream

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

Motivation

Twitter – Growth

Information Overload

http://www.cmswire.com/cms/customer-experience/35-key-twitter-statistics-infographic-012384.php
2

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

3

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

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

Filter
Apple

5

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

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

Profile Generator
Get Interested
Subscribers
RDF
A
N RDF
N Store and
O
T
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS

Profile Generator

Create Profile
8

Profile Generator
Disconnected
Social websites

Isolated data
silos

Social Networking Sites as Walled Gardens by David Simonds (Used with permission)
9

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

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

Semantic Filter
Get Interested
Subscribers
RDF
A
N RDF
N Store and
O
T
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS

Profile Generator

Create Profile
12

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

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

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

Semantic Hub
Get Interested
Subscribers
RDF
A
N RDF
N Store and
O
T
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS

Profile Generator

Create Profile
16

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

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

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

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

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

Future Work

 Periodic FOAF profile generation for users
 Twitter Stream reflecting the changing interests

 Extending to other social networks (G+, FB)

22

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.
23

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

26

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

27

Personalized Filtering of Twitter Stream

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Editor's Notes