Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams

DelftUniversity ofTechnology

Twitter, Twinder, Twitcident: Filtering and Search on Social Web StreamsData Bridges Workshop, Inria, Paris, April 12th 2012

Fabian Abel, Claudia Hauff, Geert-Jan Houben, Richard Stronkman, Ke Tao

Web Information Systems, TU Delft, the Netherlands

2Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams

200,000,000number of tweets published per day

3

Pukkelpop 2011

People tweet about everything,

everywhere :-)

4

Pukkelpop 2011

81,000 tweets in four hours

became a tragedy

Filtering

200,000,000

Search & Browsing


1. (Automatic) Filtering: Given a topic (e.g. expressed via some keywords), how can one automatically identify those tweets that are relevant to the topic?

2. Search & Browsing: How can one improve search and browsing capabilities so that users can explore information in the streams of tweets (that are relevant for a topic)?

Twitter streams

Challenges

Filtering

topic

Search & Browsing

information need

Twinder filtering and search framework


1. Filtering of Twitter streams

Twitter streams

Filtering

topic

Search & Browsing

information need


Filtering on Twitter Query: www2012

Typical approach:Keyword-based matching

Are there further features that can be used as indicators for estimating the relevance of a

tweet for a topic?


Syntactical feature: hashtagsIs a tweet more relevant if it contains a #hashtag?

＃ Hashtag

Hypothesis: tweets that contain hashtags are more likely to be relevant than tweets that do not contain hashtags.


Syntactical feature: URLsIs a tweet that contains a URL more relevant?

URL

Hypothesis: tweets that contain a URL are more likely to be relevant than tweets that do not contain a URL.


Syntactical feature: “mentions”Is a tweet that mentions @somebody more relevant?

@mention

Reply

Hypothesis: tweets that are formulated as a reply to another tweet are less likely to be relevant than other tweets.


Syntactical feature: lengthDoes the length of a tweet influence its relevance for a topic?

Hypothesis: the longer a tweet, the more likely it is to be relevant and interesting.

54 characters (9 words)

140 characters (20 words)

vs.


Overview of features

Topic sensitive Topic insensitive

Keyword-based relevance

Syntactical features

Topic-sensitive and topic-insensitive features

What about the semantics?


Semantic features: number of entities

Find semantics in a tweet to estimate the relevance

dbp:France

dbp:Tim_Berners-Lee dbp:World_Wide_Web

dbp:Lyon

dbp:WWW_Conference

Hypothesis: the more entities a tweet mentions, the more likely it is to be relevant and interesting.


I plan to visit Paris, Bordeaux, Grenoble, Nice, Marseille and Lyon.

Semantic features: diversityThe types of entities that are featured by a tweet matter

Person Thing

PlacePlace

Event

Hypothesis: the higher the diversity of entities that are mentioned in a tweet, the more likely it is to be relevant.

Place Place

Place Place Place Place

vs.


Semantic features: sentimentOpinions expressed in tweets are interesting

Hypothesis: the likelihood of a tweet’s relevance is influenced by its sentiment polarity.

Looking forward to the WWW conference :-) Yes!

I plan to visit Paris, Bordeaux, Grenoble, Nice, Marseille and Lyon.

Why are the big players not releasing query logs to the WWW community? :-( #fail

:-) neutral :-(vs. vs.


Semantic relatednessExploit semantics to relate query with tweets

Query : “TBL WWW keynote”

dbp:Tim_Berners-Lee

dbp:International_World_Wide_Web_Conference


Overview of features


Keyword-based Syntactical

Semantic-based Semantics

Context? Context?

By now, we have 4 types of features.

What kind of contextual features might be helpful?


Contextual feature: authority of the publisherIt matters who published a tweet

Hypothesis: the higher the number of tweets that have been published by the creator of a tweet, the more likely it is that the tweet is relevant.


Contextual feature: time w.r.t. query

When was a tweet published?

Hypothesis: the lower the temporal distance between the query time and the creation time of a tweet, the more likely is the tweet relevant to the topic.

Tweet

March 31

query

April 16


Summary of Features


Keyword-based Syntactical

Semantic-based Semantics

Context-based Context

21Challenge the future

without semantics 0.3363 0.4828 0.3965

all features 0.3674 0.4736 0.4138

ResultsAchieved for the TREC Microblog Challenge

Features Precision Recall F-measure

keyword relevance

0.3040 0.2924 0.2981

semantic relevance

0.3053 0.2931 0.2991

Overall, we can achieve the precision and recall of over 35% and 45% respectively by applying all the features.

22Challenge the future

Importance of Features

hasHashtag hasURL isReply length

-1

0

1

2


-1

0

1

2

Relevance Relatedness

-1

0

1

2

#entities diversity sentiment

-1

0

1

2

Topic-sensitive


-1

0

1

2

Temporal context Keyword-based relevance

-1

0

1

2

Social context

Topic-insensitive

Keyword-based

Semantic-based

Context-based

Syntactical

Semantics

Context

Semantic relatedness, URLs, !isReply, diversity and

sentiment are good indicators for estimating the

relevance of a tweet.


2. Search & Browsing in Twitter Streams

Twitter streams

Filtering

topic

Search & Browsing

information need


Idea: Faceted Search

Locations more...

Events more...

Music Artists:+ Guilty Simpson+ Bryan Adams+ Elton John+ Golden Earringmore...

Current Query:

Results:1. Yskiddd: Next saturday

@thatsimpsonguy aka Guilty Simpson will be performing at Area51 in my homeytown Eindhoven. #realliveshit #iwillspinrecords2

2. Usee123: Cool #EV3door7980 !!! http://bit.ly/igyyRhL

3. sanmiquelmusic: This Saturday I'm joining @KrusadersMusic to Intents

Eindhoven Music

Expand Query:

Suggestions:+ Guilty Simpson+ Area51


Adaptive Faceted Search

Adaptive Faceted Search

Twitter posts

Semantic Enrichment

User and Context Modeling

user

How to adapt the facet-value pair ranking to the

current demands of the user?

query suggestions

How to represent the content of a

tweet? facet extraction


Facet Extraction and Semantic Enrichment

@bob: Julian Assange got arrested http://bit.ly/5d4r2t

Julian Assange

Julian Assange Tweet-basedenrichment

Julian Assange arrestedJulian Assange, the founder ofWikiLeaks, is under arrest inLondon…

Link-basedenrichment

Julian Assange

London

WikiLeaks

Julian Assange Julian Assange

LondonWikiLeaks

powered by


Faceted-search vs. hashtag-based (keyword) search

Faceted search based on semantic

enrichment of tweets outperforms

hashtgag-based search significantly.


Impact of link-based enrichmentPersonalized strategy outperforms baseline

significantly

Link-based enrichment improves quality for

both strategies


Twitcident: Applying filter & search functionality for distilling information from Twitter during incidents (e.g. fires, extreme weather situations)

Twitter streams

Filtering

topic

Search & Browsing

information need

Twitcident application

30

Pukkelpop 2011

81,000 tweets in four hours

became a tragedy

Filtering

200,000,000

Search & Browsing


Twitcident Pipeline

Automatic Filtering

Search & Browsing

32

Filtered Twitter stream

Faceted Search

33

Real-time visualizations

34

Could we see it coming?

Term usage 25 minutes before the incident

1. heavy weather, hail balls, lightning, pitch black…2. drama, panic, hell, serious, extreme…

Impact storm

“ ”

Popular artist made a joke

about the weather

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Spotting eye witnesses

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Real-time information from eyewitness


SummaryAutomatic Filtering of Tweets: • Topic-sensitive and topic-insensitive features • Semantic features (semantic relatedness, diversity,

sentiment are beneficial)

Search and browsing:• Faceted Search• Personalization & contextualization helps

Application:• Twitcident: fulfilling information needs during incidents

Future works:• Weak signal detection based on tweets• Duplicate detection

[ISWC ’11]

[Hypertext ‘12, Demo@WWW’12]

[#MSM@WWW ’12]


Thank you!

@fabianabelhttp://wis.ewi.tudelft.nl/

Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams

Technology

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