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Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web
1. Semantic Enrichment of Twitter
Posts for User Profile
Construction on the Social Web
Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao
{f.abel, q.gao, g.j.p.m.houben, k.tao}@tudelft.nl
Web Information Systems
Delft University of Technology
Delft
University of
Technology
2. Problems of todayâs Web Systems
Hi, Iâm your new
user. Give me
personalization! System A
profile
System B Hi, I have a ?
new-user problem!
profile
profile
System C Hi, Iâm back and
I have new
interests.
profile
How can we tackle these problems?
System D
Hi, I donât know your
time
profile
current demands!
Semantic Enrichment of Twitter Posts for User Profile Construction 2
3. What we do:
news personalized
E-learning
recommendation search
User modeling with rich semantics
Analysis and User interested in:
Modeling people topics events âŚ
Linking microblog posts &
external resources
Linkage,
Semantic Enrichment Semantic Enrichment
-⯠topic detection
-⯠entity recognition & identification
user data
Microblogging
Semantic Enrichment of Twitter Posts for User Profile Construction 3
4. Research Questions
User A
Francesca Schiavone is
sportsman of the year
#sport #tennis
â˘âŻ Twi%er
 posts
 (Tweets)
Â
â˘âŻ short:
 up
 to
 140
 characters
 SI Sportsman of the year:
Surprise French Open
â˘âŻ Including
 various
 topics
 champ
Francesca Schiavone
User B
@hillhulse In an era where
Thirty in women's tennis is power players rule, I am
primordially old, an age when happy that francesca
agility and desire recedes as
the next wave of younger/
schiavone is becoming
â˘âŻ -Ââ>Ques%on1:
 Can
 we
 provide
 meaningful faster/stronger players sport idol of the year!
encroaches. It's uncommon for
 user
 proďŹles
 from
 Twi%er
 acCviCes?
 any athlete to have a
breakthrough season at 30, but
it's exceedinglyâŚ
news article
User C
â˘âŻ -Ââ>Ques%on2:
 Can
 we
 re-Ââuse
 the
 Tiw%er nice! http://bit.ly/eiU33c
-Ââbased
 proďŹles
 for
 other
 applicaCon,
 such
 as
 personalized
 recommendaCon?
Â
Semantic Enrichment of Twitter Posts for User Profile Construction 4
5. SI Sportsman of the
year: Surprise French
user Open champ
Francesca Schiavone
@hillhulse In an era where power Thirty in women's tennis is primordially
players rule, I am happy that old, an age when agility and desire
francesca is becoming #sport recedes as the next wave of younger/
idol of the year! faster/stronger players encroachesâŚ
microblog post news article
linkage
oc:SportsGame
enrichment enrichment
topic:Tennis
event:FrenchOpen
topic:Sports topic:Sports
person:Francesca_Schiavone
user modeling
Profile
Topics of interest:
- topic:Tennis
- topic:Sports
People of interest:
- person:Francesca_Schiavone
Events of interest:
- event:FrenchOpen
Semantic Enrichment of Twitter Posts for User Profile Construction 5
6. news
E-learning Public data
recommendation
Analysis and User
Modeling
Linkage,
Semantic Enrichment
user data
Microblogging
Semantic Enrichment of Twitter Posts for User Profile Construction 6
7. Linkage
Semantic Enrichment of Twitter Posts for User Profile Construction 7
8. Linkage Discovery
â˘âŻ Content-based
â˘âŻ using all of the words to as an search query and apply TF*IDF to rank the news articles
â˘âŻ Hashtag-based
â˘âŻ using hashtag(s) to search the related news articles
â˘âŻ URL-based â whether a Twitter message contain news-related URL(s)
â˘âŻ URL-based (Strict): only consider content of the Twitter message
â˘âŻ URL-based (Lenient): also consider reply or re-tweet messages
â˘âŻ Entity-based
â˘âŻ using entity(s) to search the related news articles
â˘âŻ Temporal constrain (for content-, hashtag-, and entity-based)
â˘âŻ Tweets and news articles should be published in a certain time span
Semantic Enrichment of Twitter Posts for User Profile Construction 8
9. Linkage Discovery
SI Sportsman of the year:
Surprise French Open champ
Francesca Schiavone
nice! http://bit.ly/eiU33c URL-based Thirty in women's tennis is
primordially old, an age when
agility and desire recedes as the
next wave of younger/faster/
stronger players encroaches. It's
uncommon for any athlete to
have a breakthrough season at
30, but it's exceedinglyâŚ
news article URL
d
-b ase
E n tity
Olympic champion and world
Francesca Schiavone is number nine Elena
Dementieva announced her
sportsman of the year retirement
#sport #tennis TemponE ral y-b
tit coas The 29-year-old Russian delivered
nstd ain
er the shock news after losing to
Francesca Schiavone in the group
publish date
stages of the season-ending
Old news ď tournamen âŚ
news article publish date
Semantic Enrichment of Twitter Posts for User Profile Construction 9
10. Analysis and Evaluation on Linkage Discovery and
Semantic Enrichment
â˘âŻ Evaluation on the performance of the strategies for linkage
â˘âŻ Analysis on the impact of linkage discovery on semantic enrichment of
Twitter posts
ITEM VALUE
Crawling time three weeks
Users 48,927
Tweets > 2m
News Media / News More than 60/ 77,544
Semantic Enrichment of Twitter Posts for User Profile Construction 10
11. Dataset - overview
â˘âŻ a power-law-like distribution for
the number of tweets per user
â˘âŻ c.a. 500 users were highly
active during the observation
period
Semantic Enrichment of Twitter Posts for User Profile Construction 11
12. Evaluation on Linkage Discovery
JKL234516%750<DI0=% !"*!(,%
â˘âŻ 1427 tweet-news pairs were randomly selected
JKL234516%7H1/D1/0=% !")*+%
@/A0B234516% !")#$$%
â˘âŻ Human experts rate the relations with a scale
@/A0B234516%7CD0?.E0%01FG.<4H%I./50<4D/05=%
between 1 (ânot relatedâ) and 4 (âperfected !"&'()%
matchâ) >45?049234516% !"&!'$%
-./01/0234516%78492.:2;.<65=% !"#!#$%
!% !"#% !"'% !"$% !"&% !"(% !"+% !")% !"*% !",%
!"#$%&%'()
Semantic Enrichment of Twitter Posts for User Profile Construction 12
13. Analysis on Linkage Discovery and Semantic
Enrichment
â˘âŻ URL-based strategy: more than 10 tweet-news
relations for c.a. more than 1000
â˘âŻ Entity-based strategy: found a far more higher
number of tweet-news relations
â˘âŻ Hashtag-based strategy failed for more than
79% of the users because of the limited
usage of hashtags
â˘âŻ Combined all strategy: higher than 10 tweet
-news relation found for more than 20% of
the users
Semantic Enrichment of Twitter Posts for User Profile Construction 13
14. news
E-learning Public data
recommendation
Analysis and User
Modeling
Linkage,
Semantic Enrichment
user data
Microblogging
Semantic Enrichment of Twitter Posts for User Profile Construction 14
15. User Profile Construction
event:FrenchOpen
topic:Tennis User modeling with rich semantics:
interested in:
linkage people topics events âŚ
user profile construction
#sport
temporal
Profile types enrichment
constrains
person:Francesca_Schiavone â˘âŻhashtag- â˘âŻtweet-only
â˘âŻspecific time
based â˘âŻexploitation of
period
â˘âŻtopic-based external news
â˘âŻtemporal pattern
â˘âŻentity-based resources
â˘âŻNo constrains
topic:Sports
time
weekday weekend
Semantic Enrichment of Twitter Posts for User Profile Construction 15
16. Analysis of Profile Characteristics
Entity-based profiles Topic-based profiles
10000 News-based News-based
Tweet-based
distinct topics per user proďŹle
Tweet-based
entities per user proďŹle
1000 10
100
10
0
0
1 10 100 1000 1 10 100 1000
user proďŹles user proďŹles
By exploiting the linkage between tweets and news articles, we get
more distinct entities / topics (semantics)!
Semantic Enrichment of Twitter Posts for User Profile Construction 16
17. Analysis of Profile Characteristics
number of hashtags per user proďŹle
10000 entity-based (news)
hashtag-based
1000
100
10
1
1 10 100 1000
user proďŹles
By extracting semantics from tweets and news articles, we get
richer user profiles!
Semantic Enrichment of Twitter Posts for User Profile Construction 17
18. Recommender Experiment
â˘âŻ Personalized news recommendation â recommending new articles
that fit into userâs interests[1]
semantic enrichment improves the Exploiting linkage improves the quality
quality of recommendation of recommendation.
[1] Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao. Analyzing User Modeling on Twitter for Personalized News Recommendations. In
Proceedings of International Conference on User Modeling, Adaptation and Personalization (UMAP), Girona, Spain, Springer, 2011
Semantic Enrichment of Twitter Posts for User Profile Construction 18
19. Conclusions and Future Work
â˘âŻ Twitter-based user modeling framework
â˘âŻ exploiting linkage between tweets and external news resources
â˘âŻ extract semantics from content of both tweets and news resources
â˘âŻ various design dimensions for user profile construction
â˘âŻ Evaluation and analysis on linkage discovery
â˘âŻ good performance with respect to precision and coverage
â˘âŻ Evaluation Analysis on user profile construction
â˘âŻ Richer (semantic!) user profiles
â˘âŻ constructed profiles for external application - improved accuracy of news
recommendations with enriched user profiles
â˘âŻ Future work
â˘âŻ Temporal dynamic of Twitter-based user profiles and its impact on
personalization
Semantic Enrichment of Twitter Posts for User Profile Construction 19
20. Thank You!
q.gao@tudelft.nl
Twitter: @qigaosh
Qi Gao
http://wis.ewi.tudelft.nl/tweetum/
Semantic Enrichment of Twitter Posts for User Profile Construction 20