A Framework Concept for Proﬁling Researchers on Twitter using the

Web of Data

Selver Softic

, Martin Ebner

, Laurens De Vocht

, Erik Mannens

and Rik Van de Walle

Department for Social Learning, Graz University of Technology, Graz, Austria

Department of Electronics and Information Systems - Multimedia Lab, Ghent University - iMinds, Ghent, Belgium

Keywords:

Research 2.0, Science 2.0, Web 2.0, Semantic Web, Social Media, Linked Data, Proﬁling, Twitter, Microblogs,

Web Mining.

Abstract:

Based upon ﬁndings and results from our recent research (De Vocht et al., 2011) we propose a generic frame-

work concept for researcher proﬁling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. In-

tensive growth of users in social networks, such as Twitter

∗

generated a vast amount of information. It has

been shown in many previous works that social networks users produce valuable content for proﬁling and

recommendations (Reinhardt et al., 2009; Java et al., 2007; De Vocht et al., 2011). Our research focuses

on identifying and locating experts for speciﬁc research area or topic. In our approach we apply semantic

technologies like (RDF

†

, SPARQL

‡

), common vocabularies (SIOC

, FOAF

, MOAT

, Tag Ontology

∗∗

) and

Linked Data

††

(GeoNames

‡‡

, COLINDA

) (Berners-Lee, 2006; Bizer et al., 2012) .

1 INTRODUCTION

Emergence of Social Web evolved many web content

producing communities. However information gen-

erated in them still resides in isolated ”data silos”.

The main reason for this is lack of standardized ap-

proaches for data interlinking. The Semantic Web

Technology has well deﬁned stack where appliance

of common semantic vocabularies to model data such

as SIOC (Semantically Interlinked Online Commu-

nities) (Breslin et al., 2005) and FOAF (Friend-Of-

A-Friend) leads to generation of interlinked and se-

mantically rich knowledge tanks (Bojars et al., 2008).

This knowledge is built upon user proﬁles and the

content they produce. Structuring e.g. microblog in-

formation offers potentials on qualitative mining of

such data.

Methodology proposed in this paper relies on

∗

http://www.twitter.com

†

http://www.w3.org/TR/rdf-concepts/

‡

http://www.w3.org/TR/rdf-sparql-query/

http://rdfs.org/sioc/spec/

http://www.foaf-project.org/docs/specs

http://moat-project.org/ontology

∗∗

http://www.holygoat.co.uk/projects/tags/

††

http://linkeddata.org/

‡‡

http://www.geonames.org/

http://datahub.io/dataset/colinda

three main steps: The ﬁrst step is called ”tripliﬁca-

tion” or ”RDFization” where data is extracted and an-

notated using vocabularies SIOC, FOAF, MOAT and

Tag Ontology. The RDF triples as result of this pro-

cess are stored and made accessible as linked graph

instances. The ﬁnal step includes the publication of

the data various formats via SPARQL endpoint in or-

der to provide a data for mining, which is state of

the art practice in Semantic Web domain (Bizer et al.,

2012; Tummarello et al., 2007; De Vocht et al., 2011).

Hereby vocabularies modeling the domain context,

used at the same time for structuring and description

enable more profound insights on the nature of RD-

Fized data.

Twitter as most known microblog produces 190

million Tweets and 1.6 billion search queries each

day

(2012). Further it is widely accepted in scien-

tiﬁc community for communication e.g. at confer-

ences or for discussion purposes (Boyd et al., 2010;

Jansen et al., 2009; Zhao and Rosson, 2009; Ebner

et al., 2011) what makes it reliable base for researcher

proﬁling process. However Twitter API has some

limitation which means that single user timeline in-

cludes only last 250 tweets. In order to consider those

researchers who tend to tweet more often an alter-

http://thesocialskinny.com/100-social-media-statistics-

for-2012/

447

Softic S., Ebner M., De Vocht L., Mannens E. and Van de Walle R..

A Framework Concept for Proﬁling Researchers on Twitter using the Web of Data.

DOI: 10.5220/0004369504470452

In Proceedings of the 9th International Conference on Web Information Systems and Technologies (WEBIST-2013), pages 447-452

ISBN: 978-989-8565-54-9

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

native which includes also previous tweets must be

provided.As possible solution to overcome the limits

of Twitter API a tool called Grabeeter

uhlburger

et al., 2010) has been implemented by Graz Univer-

sity of Technology. This application serves preserva-

tion of social data from Twitter. Grabeeter includes

at this certain moment about 1700 proﬁles mostly

from researchers and students and contains currently

around 6 Million tweets.

This paper describes the concept architecture for

the researcher proﬁling framework. It is aiming at

gaining more knowledge and mining usable data out

of the social context of microblogs for researcher pro-

ﬁling using ﬁndings from (Reinhardt et al., 2009; Java

et al., 2007; Letierce et al., 2010; Boyd et al., 2010;

Honeycutt and Herring, 2009; De Vocht et al., 2011).

2 RELATED WORK

The importance of microblogs, in recent years, is

gaining on importance signiﬁcantly every day (Zhao

and Rosson, 2009). Most favored among them is

Twitter, which induced a new culture of communica-

tion (McFedries, 2007; Java et al., 2007). Restricted

140 characters long Twitter messages are compara-

ble with a short message internet-based services. Java

et al. (Java et al., 2007) deﬁned four main user be-

haviors why people are using Twitter: for daily chats,

for conversation, for sharing information and for re-

porting news. Twitter is generating vast of tweets and

search queries each day. According to recent reports

(2012), Twitter has over 225 million users. Around

50 milliion of them use the Twitter each day. This

makes the Twitter worth to be researched more into

detail (Kwak et al., 2010). Usage of Twitter at confer-

ences helps to increase information awareness around

the event as well it supports spontaneous conversation

between the conference participants, which can be

used for networking and experience exchange. Nowa-

days very often so called conference related Twitter-

streams based upon a hashtag search reﬂect the on-

going occurrences within the actual event (Reinhardt

et al., 2009). Twitter info-walls placed at the confer-

ence location also support the conference administra-

tion, communication and discussion between the sci-

entiﬁc tracks and sessions(Boyd et al., 2010; Jansen

et al., 2009; Zhao and Rosson, 2009; Ebner et al.,

2011). Applied in this manner, microblogging be-

comes a valuable reporting and exchange service.

http://grabeeter.tugraz.at

http://thesocialskinny.com/100-social-media-statistics-

for-2012/

This ﬁnding is also conﬁrmed various different pub-

lications before (Reinhardt et al., 2009; Ebner et al.,

2010).

Communicational patterns in microblogs are eas-

ily mappable into a tripartite structure (De Vocht

et al., 2011; Mika, 2005). Tripartite relations to data

corresponds to the basic idea of RDF Framework and

graph based data relation. Regarding Twitter, recently

there have been some efforts like Semantic Tweet

bring the data about Twitter users into a wise semantic

form. In current research efforts for mapping of rela-

tions between the users, widely used FOAF (FrienOf-

A-Friend) vocabulary is recommended to be used, and

it will be considered by our architecture paradigm.

For posts description and relations around microblogs

like topic, author, content Semantic Web community

offers a vocabulary called SIOC (Semantically In-

terlinked Online Communities) (Bojars et al., 2008;

Breslin et al., 2005) along with Dublin Core

. Deal-

ing with tags established MOAT(Passant, 2008) and

Tag Ontology as good ontologies in this realm (Softic

et al., 2009). Currently there are also some scientiﬁc

projects that adress the issue of semantic microblog-

ging platforms. Most remarkable of them is named

(Semantic MicrOBlogging) or recently also known as

SMOB2 (Passant et al., 2010; Passant et al., 2008). It

provides a SPARQL API and relies on vocabularies

like FOAF, SIOC, MOAT and OPO (Online Presence

Ontology)

. Additionally it offers interfaces to the se-

mantic search engines like Sindice

and to the Linked

Data Cloud (LOD). Twitter based User Modeling Ser-

vice (TUMS)

infers semantic user proﬁles from the

tweet messages. This platform provides a topic de-

tection and entity extraction for tweets. Further it al-

lows an enrichment by linking tweets to news arti-

cles related to the context of these tweets (Tao et al.,

2011). According to the emerging trends, there are

some proven domain vocabularies as FOAF, SIOC,

DC (Dublin Core)

, MOAT, Tag Ontology or seman-

tic retrieval standard protocols like SPARQL provided

by the Semantic Web Community, which can be used

for semantic description and quering of semantically

enriched microblog data from Twitter (Mendes et al.,

2010; Softic et al., 2010). For description of con-

ference data through label, description, start and end

date and location SWRC (Sure et al., 2005) Ontology

along with the GeoNames Ontology covers all needs

for COLINDA as primary mining source.

http://semantictweet.com/

http://dublincore.org/documents/dcmi-terms/

http://online-presence.net/ontology.php

http://sindice.com/

http://wis.ewi.tudelft.nl/tums/

http://dublincore.org/documents/dces/

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448

Linked Data movement (Berners-Lee, 2006)

turned meanwhile the LOD Cloud (Linking Open

Data Cloud) (Bojars et al., 2008; Bizer et al., 2012)

as result of it into a reliable data source of graph

based data offering data sets like e.g. GeoNames

The GeoNames is a semantic version of a location

service. For identifying conferences, linked data set

called COLINDA (COnference LInked DAta)

of-

fers a appropriate SPARQL endpoint

. COLINDA is

linked to GeoNames and contains information about

conference name, label, description, location and time

when this event happened. Similar data sets also exist

about books, publications, science etc. Linking se-

mantic sources using simple principles described in

(Berners-Lee, 2006; Bizer et al., 2012) turns the web

into a large database, not only available for human,

but also to intelligent agents (Bojars et al., 2008).

Bringing implicit knowledge from Twitter data into

this infrastructure would enhance the LOD Cloud and

offer solid information base for research on Research

2.0 and Science 2.0 issues.

3 ARCHITECTURE

3.1 Use Case and Design Speciﬁcation

The main use case for framework is illustrated by:

”the conference case” depicted in Figure 1 already

presented in our previous work (De Vocht et al.,

2011). The idea of this scenario resides on fact that

researchers are interested in very speciﬁc topics and

events and that most of them report about these events

via blogs or tweets (Reinhardt et al., 2009; Ebner

et al., 2010) what creates huge opportunities for pro-

ﬁling. Agile development suggests to work with use

Researchers

Researcher Proﬁling

Researcher

(User)

User Model Event Model

Scientiﬁc

Conferences

Resource

Proﬁler/

Analyzer

Figure 1: Use Case for ”Researcher Proﬁling” as proposed

in (De Vocht et al., 2011).

cases. The framework has to support at least the ”Re-

searcher Proﬁling” application that meets the require-

ments to the use case presented in ﬁgure 1. Accord-

ing to the current research work about semantic ex-

traction frameworks (Softic et al., 2010) for data min-

ing the conceptual design should include three basic

http://www.colinda.org

http://data.colinda.org/endpoint.php

layers: a data extraction layer, an interlinking layer

and an analysis layer.

Extraction Layer. Extracts data from various several

data sources and describes and relates them to a spe-

ciﬁc data context using the ontologies.

Interlinking Layer. Is feeded with annotated data

(triples) and creates a SPARQL endpoint for it. It is

responsible for requesting more data if needed for a

certain information query.Futher it interpres and han-

dles high level queries and translates them from/to

SPARQL.

Analysis Layer. Deliver the results from interlinking

layer adding some metrics to rank and evaluate the

returned results.

3.2 Extraction

The extraction layer collects data from a proﬁle from

Twitter and the Grabeeter (M

uhlburger et al., 2010)

and maps then into two models: the ”User Microblogs

Model” and the ”User ProﬁleModel”. The ”User Mi-

croblog Model” gathers all data from the tweets it gets

from Grabeeter and describes them semantically us-

ing SIOC and Dublin Core vocabularies. The ”User

Proﬁle Model” is built upon Twitter user proﬁle data

with FOAF ontology. If a user not exists in Grabeeter,

then the user proﬁle and microblogs will be retrieved

directly from Twitter. The data from Twitter is re-

trieved with the help of a Twitter API

. Finally, hash-

tags are identiﬁed by simple regular expressions and

linked to the microblog data (text, creation date, au-

thor) and user proﬁle( author data, social connections)

using the Tag Ontology. These models serve a com-

ponent named ”Tripliﬁer”, that creates semantic in-

stances of graph by assembling the data using relevant

entities from ontologies. The result of the extraction

is a collection of forms semantically annotated data

into triples that describe the tweet wise content with

time stamps and links to user proﬁle. These triples are

ﬁnally stored in a RDF Store. Figure 2 illustrates this

extraction layer.

How the result of this module does look like at the

end of the process can be seen in listing 1.

3.3 Interlinking

The interlinking layer accesses the stored triples cre-

ated in the extraction layer via SPARQL protocol and

tries to interlink them to COLINDA and GeoNames.

It is impossible to create a generic framework that

covers all data domains, but we can create a system

that supports a broad range of use cases for a speciﬁc

domain like e.g. research. For now we are focusing on

https://dev.twitter.com/docs/api

AFrameworkConceptforProfilingResearchersonTwitterusingtheWebofData

449

Figure 2: Extraction layer.

Listing 1: Sample of RDFized data from microblog in N3

notation.

<http://twitter.com/someuser/status/21606926237>

rdf:type sioct:MicroblogPost ;

sioc:content

”Great talk about #web #intelligence” @ #WEBIST2012 by @otheruser”;

sioc:has creator <http://twitter.com/someuser/> ;

foaf:maker <http://grabeteer.tugraz.at/foaf/someuser/> ;

dcterms:created ”2012−11−19” ;

rdfs:sameAs <http://grabeeter.tugraz.at/tweet/199272> ;

tag:tagName ”WEBIST2012” ;

tag:tagName ”web” ;

tag:tagName ”intelligence” ;

tag:taggedResource

<http://twitter.com/someuser/status/21606926237> .

<http://twitter.com/someuser/>

rdf:type foaf:Person ;

foaf:name ”Some User” ;

foaf:depiction

<http://a0.twimg.com/p img/someuser.jpg> ;

foaf:knows <http://twitter.com/friend x y/> ;

”Researcher Proﬁling”, thus we distinguish two basic

entities:

User. Social Microblogs, annotated data from twit-

ter users (SIOC, FOAF, Dublin Core,Tag Ontology).

Since we are doing proﬁling, data from the user is an

absolute must.

Domain. Scientiﬁc Conferences, annotated data

of scientiﬁc conferences (COLINDA) to enable the

framework to recognize and link to conferences and

scientiﬁc events.

Current state of Interlinking layer is depicted in Fig-

ure 3.

This module can also handle simple requests such as

”give me tags for a user”, ”describe a user”, ”give me

all friends of a user” same as Twitter API however

the retrieved data is represented as RDF graph triples

beside the state of the art formats like XML, CSV or

JSON. Further it tries to identify which tags are sci-

entiﬁc conferences. Finally the general knowledge is

added and veriﬁed by linking tags or entities of con-

ferences that occur in the result set from Linked Open

Data. This process happens in the ”OpenDataLinker”

module. Adding additional Linked Data sets to this

Figure 3: Interlinking layer.

layer extends the appliance range of this framework.

For now framework interprets following concepts that

a researcher may be interested like: ”persons”, ”top-

ics”, ”events” and ”locations”. The interlinking layer

translates queries concerning these concepts via the

Linked Data sets, in our case COLINDA as shown in

listing 2.

Listing 2: Retrieving conference location from COLINDA

as proposed in (De Vocht et al., 2011).

SELECT ∗

{

?x rdfs:label ”WEBIST2012”;

swrc:location ?loc.

OPTIONAL

{

?loc gn:name ?city;

gn:countryName ?country;

geo:lat ?lat;

geo:long ?long.

}

As result of interlinking process a hashtag that was

extracted by tripliﬁcation and tagged by Tag Ontol-

ogy, if the matching with conference label has been

detected, is attached to the COLINDA linked data set

using the MOAT Ontology as presented in 3

Listing 3: Tagging recognised conference into RDF graph.

moat:tagMeaning <http://colinda.org/connference/123>;

tag:tagName ”WEBIST2012” ;

tag:taggedResource <http://twitter.com/someuser/status/21606926237> .

In this way a link to the conference enables the res-

olution of all conference related aditional data, like

description, date or conference location regarding the

attached proﬁle and microblog content.

3.4 Analysis and Result Delivery

The analysis is currently limited to a demonstration

where two twitter users can be compared based on

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450

similar hashtags they use. An evaluation rating is now

simply the ”Cosine Similarity” between the two sets

of hashtags. Further identiﬁed matched conferences

with all additional data like topic, event and location

are attached to result. The identiﬁcation will just use

the hashtag and see if it matches a conference name

or abbreviation. Then a new metric giving weights to

this conference matches will be calculated. A screen

shot of a demo that uses the mining functionality of

the proﬁling framework can be seen in Figure 4. The

ranking is evaluated by simple count of corresponding

entities. A similar function in framework is applicable

for other topic, locations, links, mentions and friends.

Figure 4: Demo matching.

4 CONCLUSIONS AND FUTURE

WORK

Approach presented in this paper, aims at mining

usable information out of social microblogs, with a

framework driven methodology. It is based upon Se-

mantic Web standards and Linked Data. Introducing

the interesting aspects about microblogs, authors tried

to answer how far they this data can be used for other

research areas like Science 2.0, Research 2.0. The au-

thors also outlined the importance and relevance of

such or similar efforts by examples and arguments

from current research and with example of recent own

work. In the near future, we want to answer ques-

tions like: Which researcher ﬁt to me? Which con-

ferences that cetrain researcher visited recently and

they want to visit in the future ﬁt to my interest area?

Generation of forecast reports about scientist and spe-

ciﬁc conferences as well about upcoming conferences

that match the own research focus is also an issue

that will be supported in the future with proposed

framework. Further linking the scientists automati-

cally to sub communities based on their interests can

be a thinkable extension of proposed context. Hereby

we are aiminig at using common techiques for com-

munity distinction provided by network science like

hierarchical clustering or minimal cut ratio methods.

Considering the technical improvements we want to

expose our proof of concept implementation as REST

based API as done in previous work for interface ag-

gregation (De Vocht et al., 2011) and run some more

accurate tests on retrieval metrics like precision,recall

and F-measure in order to evauate the quality of pro-

posed solution.

ACKNOWLEDGEMENTS

The research activities that have been described in this

paper were funded by Graz University of Technology,

Ghent University, iMinds (an independent research

institute founded by the Flemish government to stim-

ulate ICT innovation), the Institute for the Promotion

of Innovation by Science and Technology in Flan-

ders (IWT), the Fund for Scientiﬁc Research-Flanders

(FWO-Flanders), and the European Union.

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