semantic enrichment of twitter posts for user profile construction on the social web

Delft University of Technology

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

2 Semantic Enrichment of Twitter Posts for User Profile Construction

System A

time

Hi, I’m your new user. Give me

personalization!

Hi, I have a new-user problem!

profile

?

profile

Hi, I don’t know your current demands!

Hi, I’m back and I have new interests.

System C

profile

System D

profile

System B

profile

Problems of today’s Web Systems

How can we tackle these problems?


What we do:

Microblogging

Analysis and User Modeling

news recommendation E-learning personalized

search

user data

Linkage, Semantic Enrichment

Linking microblog posts & external resources

Semantic Enrichment -  topic detection -  entity recognition & identification

User modeling with rich semantics interested in:

people topics events …


Research Questions

•  Twi%er posts (Tweets) •  short: up to 140 characters •  Including various topics

@hillhulse In an era where power players rule, I am happy that francesca schiavone is becoming sport idol of the year!

User A

Francesca Schiavone is sportsman of the year #sport #tennis

nice! http://bit.ly/eiU33c

User B

User C

SI Sportsman of the year: Surprise French Open champ Francesca Schiavone

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 exceedingly…

news article

•  -‐>Ques%on2: Can we re-‐use the Tiw%er-‐based profiles for other applicaCon, such

as personalized recommendaCon?

•  -‐>Ques%on1: Can we provide meaningful

user profiles from Twi%er acCviCes?


SI Sportsman of the year: Surprise French Open champ Francesca Schiavone 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…

news article

topic:Sports topic:Sports

topic:Tennis

person:Francesca_Schiavone

oc:SportsGame

event:FrenchOpen

@hillhulse In an era where power players rule, I am happy that francesca is becoming #sport idol of the year!

microblog post

user

enrichment enrichment

user modeling

linkage

Profile Topics of interest: - topic:Tennis - topic:Sports People of interest: - person:Francesca_Schiavone Events of interest: - event:FrenchOpen


Microblogging


news recommendation E-learning Public data

user data



Linkage


Linkage Discovery

•  Content-based •  using all of the words to as an search query and apply TF*IDF to rank the news articles

•  Hashtag-based •  using hashtag(s) to search the related news articles

•  URL-based – whether a Twitter message contain news-related URL(s) •  URL-based (Strict): only consider content of the Twitter message

•  URL-based (Lenient): also consider reply or re-tweet messages

•  Entity-based •  using entity(s) to search the related news articles

•  Temporal constrain (for content-, hashtag-, and entity-based) •  Tweets and news articles should be published in a certain time span


Linkage Discovery

nice! http://bit.ly/eiU33c URL-based

SI Sportsman of the year: Surprise French Open champ Francesca Schiavone

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 exceedingly…

news article URL

Entity-based

Olympic champion and world number nine Elena Dementieva announced her retirement

The 29-year-old Russian delivered the shock news after losing to Francesca Schiavone in the group stages of the season-ending tournamen …

news article

Entity-based

Francesca Schiavone is sportsman of the year #sport #tennis Temporal constrain

Old news publish date

publish date


Analysis and Evaluation on Linkage Discovery and Semantic Enrichment

ITEM VALUE Crawling time three weeks

Users 48,927

Tweets > 2m

News Media / News More than 60/ 77,544

•  Evaluation on the performance of the strategies for linkage

•  Analysis on the impact of linkage discovery on semantic enrichment of Twitter posts


Dataset - overview

•  a power-law-like distribution for the number of tweets per user

•  c.a. 500 users were highly active during the observation period


Evaluation on Linkage Discovery

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•  1427 tweet-news pairs were randomly selected

•  Human experts rate the relations with a scale between 1 (“not related”) and 4 (“perfected match”)


Analysis on Linkage Discovery and Semantic Enrichment

•  URL-based strategy: more than 10 tweet-news relations for c.a. more than 1000

•  Entity-based strategy: found a far more higher number of tweet-news relations

•  Hashtag-based strategy failed for more than 79% of the users because of the limited usage of hashtags

•  Combined all strategy: higher than 10 tweet-news relation found for more than 20% of the users


Microblogging


news recommendation E-learning Public data

user data



User Profile Construction

User modeling with rich semantics: interested in:

people topics events … linkage user profile construction

#sport

person:Francesca_Schiavone

topic:Sports

event:FrenchOpen

topic:Tennis

time

weekday weekend

Profile types

• hashtag-based • topic-based • entity-based

enrichment • tweet-only • exploitation of external news resources

temporal constrains

• specific time period • temporal pattern • No constrains


1 10 100 1000user profiles

0

10

dist

inct

topi

cs p

er u

ser p

rofil

e

News-basedTweet-based

Analysis of Profile Characteristics

By exploiting the linkage between tweets and news articles, we get more distinct entities / topics (semantics)!


0

10

100

1000

10000

entit

ies

per u

ser p

rofil

e

News-basedTweet-based

Entity-based profiles Topic-based profiles


Analysis of Profile Characteristics

By extracting semantics from tweets and news articles, we get richer user profiles!


1

10

100

1000

10000

num

ber o

f has

htag

s pe

r use

r pro

file entity-based (news)

hashtag-based


Recommender Experiment

semantic enrichment improves the quality of recommendation

Exploiting linkage improves the quality of recommendation.

•  Personalized news recommendation – recommending new articles that fit into user’s interests[1]

[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


Conclusions and Future Work

•  Twitter-based user modeling framework •  exploiting linkage between tweets and external news resources •  extract semantics from content of both tweets and news resources •  various design dimensions for user profile construction

•  Evaluation and analysis on linkage discovery •  good performance with respect to precision and coverage

•  Evaluation Analysis on user profile construction •  Richer (semantic!) user profiles •  constructed profiles for external application - improved accuracy of news

recommendations with enriched user profiles • Future work

•  Temporal dynamic of Twitter-based user profiles and its impact on personalization


Qi Gao [email protected] Twitter: @qigaosh http://wis.ewi.tudelft.nl/tweetum/

Thank You!

semantic enrichment of twitter posts for user profile construction on the social web

Technology

new user

user proles

user modeling linkage

user profile construction5

user profileconstruction

newuser problem

shock news

linkage analysis