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Volatilebased on   Volatile Classification of Point of InterestsClassificationSocial Awareness Streams               A.E. Ca...
Outline       Outline•    Introduction•    Motivation•    Contributions•    Related Work•    Geolattice Awareness Streams•...
Introduction         IntroductionSocial Awareness Streams Collection of semi-public, natural language messages produced by...
Introduction      IntroductionSocial Awareness Streams Used as a historical data set for profiling users’ activities and be...
Introduction      IntroductionContextual Meaning of Places
Introduction        MotivationPoint of Interest (POI)o  Specific point location that someone may find useful or   interestin...
Place        Introduction          Motivation An evolving set of both communal and personal labels for potentially overlap...
Introduction      MotivationSocial Awareness Streams and POIs Can social awareness streams convey information for inducing...
Introduction       MotivationPoint of Interest (POI)                                              History                H...
Introduction       MotivationPoint of Interest (POI)                Tags: looting, unrest, police..                       ...
Introduction       MotivationPoint of Interest (POI)                Tags: looting, unrest, riots, police..                ...
Related Work        Related WorkUse of SAS for Tagging Places•  Cheng et al [6]: Spatial distribution of words in tweets f...
Related Work       Related WorkUse of SAS for Tagging Places•  Ireson and Ciravegna [9]: Toponym resolution (i.e. allocati...
Related Work      Related WorkContrasting our Work  Rather than identifying words for tagging places we study how  locatio...
GeoLattice Aware      GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples      ...
GeoLattice Aware      GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples      ...
GeoLattice Aware      GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples      ...
GeoLattice Aware      GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples      ...
GeoLattice Aware      GeoLattice Awareness StreamsPOI Awareness Stream   Given a GeoLattice Awareness Stream, a POI awaren...
GeoLattice Aware            GeoLattice Awareness StreamsPOI Awareness Stream    UK	                                       ...
Transient Semant      Transient Semantic Classification of a POIProblemHow can we derive a temporal classification (Rcat) of...
Transient Semant     Transient Semantic Classification of a POIApproach Retrieve Messages from a POI Awareness Stream for a...
Transient Semant      Transient Semantic Classification of a POIApproach- Message Enrichment Retrieve Messages from a POI A...
Transient Semant      Transient Semantic Classification of a POIApproach- Message Enrichment Retrieve Messages from a POI A...
Transient Semant      Transient Semantic Classification of a POIApproach- Semantic Categorisation  Retrieve Messages from a...
Transient Semant      Transient Semantic Classification of a POIApproach- Semantic Categorisation  Retrieve Messages from a...
Transient Semant      Transient Semantic Classification of a POIApproach- Induce Category Function Retrieve Messages from a...
Transient Semant       Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a st...
Transient Semant         Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a ...
Transient Semant         Transient Semantic Classification of a POIApproach- Induce Category FunctionCategory Entropy of a ...
Transient Semant       Transient Semantic Classification of a POIApproach- Induce Category FunctionMutual Information	  Mea...
Transient Semant       Transient Semantic Classification of a POIApproach- Induce Category FunctionMutual Information	  Mea...
Enpirical Study      Empirical StudyData SetWe monitored tweets generated from the city of Sheffield, during 3weekends.Comm...
Enpirical Study      Empirical StudyData SetAfter enriching the content of the tweets and querying DBpedia wegot the follo...
Enpirical Study      Empirical StudyData SetThe ten top more frequent hashtags in the data sets were :
Results       Results Category EntropyCategory entropy for each category of each of the three data set.
Results       Results Category EntropyCategory entropy for each category of each of the three data set.
Results       Results Categories with lowest Category Entropy
Results       Results Categories with lowest Category Entropy
Results       Results Categories with lowest Category Entropy Event involving 2012 Olympic Tickets sales                  ...
Results       Results Categories with lowest Category Entropy
Results      Results Deriving Context with MITo provide a context in which the a category is used, we used themutual infor...
Results       Results Highlights  A point of interest considered as a Location-Entity presents the  “meformer” and “inform...
Conclusions     Conclusions and Future WorkWe have presented :•  A formalisation for describing geographically bounded soc...
References        References[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in socialaware...
References         References[6] Hightower, J.: From Position to Place. In: Proc. of the 2003 Workshop on Location-AwareCo...
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Volatile Classification of Point of Interests based on Social Activity Streams

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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.

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Transcript of "Volatile Classification of Point of Interests based on Social Activity Streams"

  1. 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. 2. Outline Outline•  Introduction•  Motivation•  Contributions•  Related Work•  Geolattice Awareness Streams•  Transient Semantic Classification of a POI•  Experiments•  Conclusions and Future Work
  3. 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. 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. 5. Introduction IntroductionContextual Meaning of Places
  6. 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. 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. 8. Introduction MotivationSocial Awareness Streams and POIs Can social awareness streams convey information for inducing a transient classification of a point of interest?
  9. 9. Introduction MotivationPoint of Interest (POI) History History Literature Literature Shopping Fashion Shopping Fashion Tourism Tourism
  10. 10. Introduction MotivationPoint of Interest (POI) Tags: looting, unrest, police.. History History Literature Literature Shopping Fashion Shopping Fashion Tourism Tourism
  11. 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. 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. 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. 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. 15. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft)
  16. 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. 17. GeoLattice Aware GeoLattice Awareness StreamsDefinition 1. A Geolattice Awareness Stream is a sequence of tuples S:= (Poiq1,Mq2,Rq3,Y,ft) source
  18. 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. 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. 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. 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. 22. Transient Semant Transient Semantic Classification of a POIApproach Retrieve Messages from a POI Awareness Stream for a window of time [ts-te]
  23. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 34. Enpirical Study Empirical StudyData SetAfter enriching the content of the tweets and querying DBpedia wegot the following hashtags, resources, and categories:
  35. 35. Enpirical Study Empirical StudyData SetThe ten top more frequent hashtags in the data sets were :
  36. 36. Results Results Category EntropyCategory entropy for each category of each of the three data set.
  37. 37. Results Results Category EntropyCategory entropy for each category of each of the three data set.
  38. 38. Results Results Categories with lowest Category Entropy
  39. 39. Results Results Categories with lowest Category Entropy
  40. 40. Results Results Categories with lowest Category Entropy Event involving 2012 Olympic Tickets sales Event involving Spanish Football
  41. 41. Results Results Categories with lowest Category Entropy
  42. 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. 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. 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. 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. 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.
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