Volatile Classification of Point of Interests based on Social Activity Streams

  • 958 views
Uploaded on

Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These …

Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
958
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
7
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Volatilebased on Volatile Classification of Point of InterestsClassificationSocial Awareness Streams A.E. Cano, A. Varga and F. Ciravegna The Oak Group, Department of Computer Science, The University of Sheffield
  • 2. Outline Outline•  Introduction•  Motivation•  Contributions•  Related Work•  Geolattice Awareness Streams•  Transient Semantic Classification of a POI•  Experiments•  Conclusions and Future Work
  • 3. Introduction IntroductionSocial Awareness Streams Collection of semi-public, natural language messages produced by different users and characterised by their brevity [1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.
  • 4. Introduction IntroductionSocial Awareness Streams Used as a historical data set for profiling users’ activities and behavior [2][3][4]. Murakami Michio Kaku Δt Spotify Iphone € Time
  • 5. Introduction IntroductionContextual Meaning of Places
  • 6. Introduction MotivationPoint of Interest (POI)o  Specific point location that someone may find useful or interesting.o  Characterised by static data (e.g. name, geo-coordinates)o  Classified by the type of services it provides. London, United Kingdom Coordinates: 51.507221, -0.1275 Categories:  Leisure and entertainment  Literature, film and television  Museums and art galleries  Music  Sports Source : http://en.wikipedia.org/wiki/London
  • 7. Place Introduction Motivation An evolving set of both communal and personal labels for potentially overlapping geometric volumes. A meaningful place can capture the venues’ demographical, environmental, historical, personal or commercial significance.   [5] Hightower, J.: From Position to Place. In: Proc. of the 2003 Workshop on Location-Aware Computing. (2003) 10–12 part of the 2003 Ubiquitous Computing Conf.
  • 8. Introduction MotivationSocial Awareness Streams and POIs Can social awareness streams convey information for inducing a transient classification of a point of interest?
  • 9. Introduction MotivationPoint of Interest (POI) History History Literature Literature Shopping Fashion Shopping Fashion Tourism Tourism
  • 10. Introduction MotivationPoint of Interest (POI) Tags: looting, unrest, police.. History History Literature Literature Shopping Fashion Shopping Fashion Tourism Tourism
  • 11. Introduction MotivationPoint of Interest (POI) Tags: looting, unrest, riots, police.. History History Politics Uprising Literature Literature Violence Shopping Fashion Shopping Fashion Social Crisis Tourism Tourism
  • 12. Related Work Related WorkUse of SAS for Tagging Places•  Cheng et al [6]: Spatial distribution of words in tweets for predicting users’location. Their probabilistic framework can identify words in tweets withstrong geo-scope.•  Cheng et al [7]: Mobility patterns of users in LSS. They correlate socialstatus, geographic and economic factors with mobility and performsentiment analysis of posts for deriving unobserved context between peopleand location.• Lin et al [8]: Taxonomy of ‘place naming methods’, showing that a person’sperceived familiarity with a place and the variety of people who visit it,strongly influence the way people refer to this place when interacting with others.
  • 13. Related Work Related WorkUse of SAS for Tagging Places•  Ireson and Ciravegna [9]: Toponym resolution (i.e. allocation ofspecific geo-location to target location terms) using Flickr data. Usinggeo-tag media, users’ contacts, content as features, they build an SVMclassifier for learning location labels associated to a location term.
  • 14. Related Work Related WorkContrasting our Work Rather than identifying words for tagging places we study how location-based generated content can be modelled for discovering topics or categories that classify a place in time. For this we present:   eoLattice Awareness Streams Model G   ransient Semantic Classification of a POI T
  • 15. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft)
  • 16. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft)NameGeographical-bounding areaGeo-coordinates
  • 17. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft) source
  • 18. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft) Keywords Categories Hashtags
  • 19. GeoLattice Aware GeoLattice Awareness StreamsPOI Awareness Stream Given a GeoLattice Awareness Stream, a POI awareness stream can be defined as the sequence of tuples from S where:
  • 20. GeoLattice Aware GeoLattice Awareness StreamsPOI Awareness Stream UK   Supertram Urban Zone Archeology Steel Education Bath Industrial Revolution Politics Football Westminster Modern Architecture Thames Gothic Revival
  • 21. Transient Semant Transient Semantic Classification of a POIProblemHow can we derive a temporal classification (Rcat) of a POI given it’sPOI Awareness Stream (S(Poi’)) ?
  • 22. Transient Semant Transient Semantic Classification of a POIApproach Retrieve Messages from a POI Awareness Stream for a window of time [ts-te]
  • 23. Transient Semant Transient Semantic Classification of a POIApproach- Message Enrichment Retrieve Messages from a POI Awareness Stream for a Enrich Messages window of time [ts-te]
  • 24. Transient Semant Transient Semantic Classification of a POIApproach- Message Enrichment Retrieve Messages from a POI Awareness Stream for a Enrich Messages window of time [ts-te]
  • 25. Transient Semant Transient Semantic Classification of a POIApproach- Semantic Categorisation Retrieve Messages from a Semantic POI Awareness Stream for a Enrich Messages Categorisation window of time [ts-te]
  • 26. Transient Semant Transient Semantic Classification of a POIApproach- Semantic Categorisation Retrieve Messages from a Semantic POI Awareness Stream for a Enrich Messages Categorisation window of time [ts-te]
  • 27. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category Function Retrieve Messages from a Semantic POI Awareness Stream for a Enrich Messages Categorisation window of time [ts-te] Induce Category Function
  • 28. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a stream  Indicates the topical diversity of the stream.   Low category entropy levels reveal that a stream is dominated by few categories, while a high category balance reveals a higher topical diversity.  
  • 29. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a stream Normal Conditions Normal Conditions Broken (Events Happening) Food Food Restaurant Restaurant Italian Cuisine Italian Cuisine CE low CE Increases City City CE high CE Decreases
  • 30. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a stream Normal Conditions Normal Conditions Broken (Events Happening) Food Food Restaurant Restaurant Italian Cuisine Italian Cuisine CE low CE Increases City City CE high CE Decreases
  • 31. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category FunctionMutual Information  Measures the information that two discrete random variables share. Inthis work we consider the MI between:Categories- Hashtags(MI)  Categories- Keywords(MI)   Hashtags- Keywords(MI)  
  • 32. Transient Semant Transient Semantic Classification of a POIApproach- Induce Category FunctionMutual Information  Measures the information that two discrete random variables share. Inthis work we consider the MI between:Categories- Hashtags(MI)  Categories- Keywords(MI)   Hashtags- Keywords(MI)  
  • 33. Enpirical Study Empirical StudyData SetWe monitored tweets generated from the city of Sheffield, during 3weekends.Common – 2011-06-10 to 2011-06-13Food Festival – 2011-07-08 to 2011-07-11Tramlines Music Festival – 2011-07-22 to 2011-07-25
  • 34. Enpirical Study Empirical StudyData SetAfter enriching the content of the tweets and querying DBpedia wegot the following hashtags, resources, and categories:
  • 35. Enpirical Study Empirical StudyData SetThe ten top more frequent hashtags in the data sets were :
  • 36. Results Results Category EntropyCategory entropy for each category of each of the three data set.
  • 37. Results Results Category EntropyCategory entropy for each category of each of the three data set.
  • 38. Results Results Categories with lowest Category Entropy
  • 39. Results Results Categories with lowest Category Entropy
  • 40. Results Results Categories with lowest Category Entropy Event involving 2012 Olympic Tickets sales Event involving Spanish Football
  • 41. Results Results Categories with lowest Category Entropy
  • 42. Results Results Deriving Context with MITo provide a context in which the a category is used, we used themutual information between categories and hashtags, from which weobtained a set of hashtags used to further derive related keywords(using Hashtag-Keywords MI).
  • 43. Results Results Highlights A point of interest considered as a Location-Entity presents the “meformer” and “informer” patterns observed by Naaman et al. [9] in Person-Entity activity streams.  Location-Entity Meformer pattern refers to a self focus of a Location-Entity, presenting information about endemic events.  Location-Entity Informer pattern refers to an information sharing about external events, not necessarily related to this Location-Entity.
  • 44. Conclusions Conclusions and Future WorkWe have presented :•  A formalisation for describing geographically bounded social awareness streams.•  An approach to derive transient categorisations of Points of Interest based on DBPedia Categories.Future Work:•  Determining a semantic coherence metric which could induce broader category clusters.
  • 45. References References[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in socialawareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supportedcooperative work, pages 189–192, New York, NY, USA, 2010. ACM.[2] C. Wagner and M. Strohmaier. The wisdom in tweetonomies: Acquiring latent conceptualstructures from social awareness streams. In Proc. of the Semantic Search 2010 Workshop(SemSearch2010), april 2010.[3] F.Abel,Q.Gao,G.Houben,andK.Tao.Semanticenrichmentoftwitterpostsforuserprofileconstruction on the social web. In In proceedings of Extended Semantic Web Conference 2011, May2011.[4] A. Cano, S. Tucker, and F. Ciravegna. Capturing entity-based semantics emerging frompersonal awareness streams. In Proceedings of the Workshop on Making Sense of Microposts(MSM2011), May 2011.
  • 46. References References[6] Hightower, J.: From Position to Place. In: Proc. of the 2003 Workshop on Location-AwareComputing. (2003) 10–12 part of the 2003 Ubiquitous Computing Conf.[7] Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach togeo-locating twitter users. In Proceedings of the 19th ACM international conference on Information andknowledge management, CIKM ’10, pages 759–768, New York, NY, USA, 2010. ACM.[8] K. L. D. Z. S. Zhiyuan Cheng, James Caverlee. Exploring millions of footprints inlocationsharing services. In 5th International Conference on Weblogs and Social Media (ICWSM),ICWSM ’11. ACM, 2011.[9] J. Lin, G. Xiang, J. I. Hong, and N. Sadeh. Modeling people’s place naming preferencesinlocation sharing. In Proceedings of the 12th ACM international conference on Ubiquitous computing,Ubicomp ’10, pages 75–84, New York, NY, USA, 2010. ACM.[10] N. Ireson and F. Ciravegna. Toponym resolution in social media. In Proc., 9thInternationalSemantic Web Conference. ISWC 2010, 2010.