Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams
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Slides presented at the EIT DataBridges Workshop in Paris: http://team.inria.fr/oak/share/workshop-databridges-april-12-2012/
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Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams
- 1. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams Data 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 Delft University of Technology
- 2. 200,000,000 number of tweets published per day Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 2
- 3. Pukkelpop 2011 People tweet about everything, everywhere :-) 3
- 4. 200,000,000 Pukkelpop 2011 became a tragedy Filtering 81,000 tweets in four hours Search & Browsing 4
- 5. Challenges 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)? Twinder Filtering Search & filtering Browsing and search framework Twitter streams topic information need Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 5
- 6. Search & Filtering Browsing Twitter streams topic information need 1. Filtering of Twitter streams Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 6
- 7. Filtering onTwitter 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? Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 7
- 8. Syntactical feature: hashtags Is a tweet more relevant ifitcontains a #hashtag? Hypothesis: tweets that contain hashtags are more likely to be relevant than tweets that do not contain hashtags. ＃Hashtag Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 8
- 9. Syntactical feature: URLs Is a tweetthatcontains a URL more relevant? Hypothesis: tweets that contain a URL are more likely to be relevant than tweets that do not contain a URL. URL Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 9
- 10. Syntactical feature: “mentions” Is a tweetthatmentions@somebodymore relevant? Hypothesis: tweets that are formulated as a reply to another tweet are less likely to be relevant than other tweets. Reply @mention Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 10
- 11. Syntacticalfeature: length Does the length of a tweetinfluenceitsrelevancefor a topic? 54 characters (9 words) vs. 140 characters (20 words) Hypothesis: the longer a tweet, the more likely it is to be relevant and interesting. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 11
- 12. Overview of features Topic-sensitive and topic-insensitive features Topic sensitive Topic insensitive Keyword-based Syntactical features relevance What about the semantics? Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 12
- 13. Semanticfeatures: number of entities Findsemantics in a tweettoestimate the relevance dbp:Tim_Berners-Lee dbp:World_Wide_Web dbp:WWW_Conference dbp:France dbp:Lyon Hypothesis: the more entities a tweet mentions, the more likely it is to be relevant and interesting. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 13
- 14. Semanticfeatures: diversity The types of entitiesthat are featuredby a tweet matter Place Place Place Place Person Thing vs. I plan to visit Paris, Bordeaux, Grenoble, Nice, Marseille and Lyon. Event Place Place Place Place Hypothesis: the higher the diversity of entities that are mentioned in a tweet, the more likely it is to be relevant. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 14
- 15. Semanticfeatures: sentiment Opinionsexpressed in tweets are interesting Looking forward to the WWW conference :-) Yes! vs. I plan to visit Paris, Bordeaux, vs. Grenoble, Nice, Marseille and Lyon. Why are the big players not releasing query logs to the WWW community? :-( #fail :-) neutral :-( Hypothesis: the likelihood of a tweet’s relevance is influenced by its sentiment polarity. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 15
- 16. Semanticrelatedness Exploitsemantics to relate query withtweets dbp:International_World_Wide_Web_Conference dbp:Tim_Berners-Lee Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 16
- 17. Overview of features Bynow, we have 4 types of features. Topic sensitive Topic insensitive Keyword-based Syntactical Semantic-based Semantics Context? Context? What kind of contextual features might be helpful? Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 17
- 18. Contextual feature: authority of the publisher Itmatterswhopublished 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. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 18
- 19. Contextual feature: time w.r.t. query When was a tweetpublished? 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 query March 31 April 16 Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 19
- 20. Summary of Features Topic sensitive Topic insensitive Keyword-based Syntactical Semantic-based Semantics Context-based Context Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 20
- 21. Results Achievedfor the TREC MicroblogChallenge Features Precision Recall F-measure keyword relevance 0.3040 0.2924 0.2981 without semantics semantic relevance 0.3363 0.3053 0.4828 0.2931 0.3965 0.2991 all features 0.3674 0.4736 0.4138 Overall, we can achieve the precision and recall of over 35% and 45% respectively by applying all the features. Challenge the future 21
- 22. Importance of Features Topic-sensitive Topic-insensitive 2 2 Keyword-based Syntactical 1 1 0 0 Keyword-based relevance hasHashtag hasURL isReply length -1 -1 2 2 1 Semantic-based 1 Semantics 0 0 Relevance Relatedness #entities diversity sentiment -1 -1 Semantic relatedness, URLs, !isReply, diversity and 2 Context-based Context 2 sentiment are good indicators for estimating the 1 1 0 0 relevance of a tweet. -1 Temporal context Keyword-based relevance -1 Social context Keyword-based relevance Challenge the future 22
- 23. Search & Filtering Browsing Twitter streams topic information need 2. Search & Browsing in Twitter Streams Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 23
- 24. Idea: Faceted Search Expand Query: Current Query: Suggestions: Eindhoven Music + Guilty Simpson + Area51 Results: 1. Yskiddd: Next saturday Locations more... @thatsimpsonguy aka Guilty Simpson will be performing at Area51 in my Events more... homeytown Eindhoven. #realliveshit #iwillspinrecords2 Music Artists: 2. Usee123: Cool #EV3door7980 !!! + Guilty Simpson http://bit.ly/igyyRhL + Bryan Adams + Elton John 3. sanmiquelmusic: This Saturday I'm joining @KrusadersMusic to Intents + Golden Earring more... Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 24
- 25. Adaptive Faceted Search user Adaptive Faceted Search How to adapt the How to represent facet-value pair the content of a ranking to the User and Context Modeling tweet? current demands of facet extraction the user? query suggestions Semantic Enrichment Twitter posts Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 25
- 26. Facet Extraction and Semantic Enrichment powered by Julian Assange @bob: Julian Assange got Tweet-based arrested enrichment Julian Assange Julian Assange Julian Assange arrested Link-based London Julian Assange, the founder of Julian Assange enrichment WikiLeaks, is under arrest in WikiLeaks London… London WikiLeaks Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 26
- 27. Faceted-search vs. hashtag-based (keyword) search Faceted search based on semantic enrichment of tweets outperforms hashtgag-based search significantly. Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 27
- 28. Impact of link-based enrichment Personalized strategy outperforms baseline significantly Link-based enrichment improves quality for both strategies Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 28
- 29. Twitcident application Search & Filtering Browsing Twitter streams topic information need Twitcident: Applying filter & search functionality for distilling information from Twitter during incidents (e.g. fires, extreme weather situations) Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 29
- 30. 200,000,000 Pukkelpop 2011 became a tragedy Filtering 81,000 tweets in four hours Search & Browsing 30
- 31. Search & Browsing Automatic Filtering Twitcident Pipeline Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 31
- 32. Faceted Search Filtered Twitter stream 32
- 33. Real-time visualizations 33
- 34. Could we see it coming? Popular artist made a joke Impact about the weather storm Term usage 25 minutes before the incident 1. heavy weather, hail balls, lightning, pitch black… 2. drama, panic, hell, serious, extreme… “ ” 34
- 35. Spotting eye witnesses 35
- 36. Real-time information from eyewitness 36
- 37. Summary Automatic Filtering of Tweets: [#MSM@WWW ’12] • Topic-sensitive and topic-insensitive features • Semantic features (semantic relatedness, diversity, sentiment are beneficial) Search and browsing: [ISWC ’11] • Faceted Search • Personalization & contextualization helps Application: [Hypertext ‘12, Demo@WWW’12] • Twitcident: fulfilling information needs during incidents Future works: • Weak signal detection based on tweets • Duplicate detection Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 37
- 38. Thank you! @fabianabel http://wis.ewi.tudelft.nl/ Twitter, Twinder, Twitcident: Filtering and Search on Social Web Streams 38
Notas do Editor
- Motivation:Information overloadPersonalised “better” search
- Eet en drankbonnent.w.v. 75 a 100 euro beschikbaarstellen.
- Eet en drankbonnent.w.v. 75 a 100 euro beschikbaarstellen.
- Traditional Twitter SearchHighlight what does keyword matching means, the keywords, in search query and tweets.
- Title -> syntactical featuresBox in the tweetGreen boxes for the hypothesesFlow from keyword-based relevance to … Slide 5-8, flow
- Subtitle -> question?
- Introduction to the usage of @, including mentions, and reply. Reply tweets frequently occur in private conversations. Therefore particularly, make a hypothesis about reply tweet.
- The 21st International World Wide Web Conference #www2012 will take place in Lyon, France April 16-20 2012 @www2012Lyon www2012.wwwconference.orgSubtitle questionOne short, one longcomparison
- Fade in the question later.
- Fade in the entities one by one.
- Fade in the entities one by one.
- Fade in the entities one by one.
- Not highlight www, lyon, france
- 18Can we utilize the contextual features.
- Titles,
- Timeline
- Number of features.
- ComparisonFade in the pairsHighlightTextbox -> Conclusion, (precision)
- Very time consuming and overwhelming indeed!
- entity extraction and semantic enrichment and relation discovery.
- Eet en drankbonnent.w.v. 75 a 100 euro beschikbaarstellen.
- Case #1: vroegsignalering
- Case 1:handhaving (beeldrondom incident)
- Our framework extracts typed entities from enriched tweets/news and provides strategies for detecting semantic (trending) relationships between entities. We:investigated the precision and recall of the relation detection strategies,analyzed how the strategies perform for each type of relationships andWhich strategy performs best in detecting relationships between entities?Does the accuracy depend on the type of entities which are involved in a relation?How do the strategies perform for discovering relationships which have temporal constraints, and how fast can the strategies detect (trending) relationships?evaluated the quality and speed for discovering trending relationships that possibly have a limited temporal validity.