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Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data: Challenges and Experiences

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Meenakshi Nagarajan, Karthik Gomadam, Amit Sheth, Ajith Ranabahu, Raghava Mutharaju and Ashutosh Jadhav, "Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data: Challenges and Experiences," Tenth …

Meenakshi Nagarajan, Karthik Gomadam, Amit Sheth, Ajith Ranabahu, Raghava Mutharaju and Ashutosh Jadhav, "Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data: Challenges and Experiences," Tenth International Conference on Web Information Systems Engineering, Oct 5-7, 2009, Poland.

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  • Since the paper was submitted more has happened, techniques have improved, ui has changed, submission to the ISWC challengeSo what you see here in terms of examples and UI will be different, but the analytics are the same
  • unique appeal behind microblogging is its portability and theability to post and recieve eye-witness accounts in real-time.An important sideeffect has been the rise of citizen journalism, where humans as sensors are play-ing an active role in the process of collecting, reporting, analyzing and dissemi-nating news and information"[wiki]. Today, these six billion human sensors haveresulted in an interconnected network of participatory citizens who share andreact to observations in an almost real-time manner.
  • Perhaps, the most interesting phenomenon about such citizen generated datais that it acts as a lens into the social perception of an event in any region, at anypoint in time.Consider the g20 event that sparked protests in spatial region xand something else in y. Citizen observations relayed from the same or dierentlocation oer multiple, and often complementary viewpoints or storylines aboutan event. What is more, these viewpoints evolve over time and with the occurenceof other events, with perceptions gaining momentum in certain regions afterbeing popular in some others.
  • Consequently, in addition to what is being said about an event (the theme),where (spatial) and when (time) it is being said are integral components tothe analysis of such data (see Figure 1).
  • Presentation will focus on the mashup.Not going into details of the research contributions, formula.There are more details on the paper.
  • Obtain “raw” tweetsProcessStore the information extracted from the tweets
  • Thematic processign consider tweets in each set separatelyBy doing the spatio-temporal slicing of data, we preserve these social signals that might be dependent on place and time
  • We extract “Descriptors”. In a sense they are telling us what a region is…It’s like extracting terms with high TFIDF, but we have three attributes to consider.
  • Big 3 not used as frequently as GM, Ford and Chrysler (or vice versa) Presence of contextually relevant words should strengthen the score of a descriptorGets collocated descriptors, calculate PMI
  • This pic shows many countries together. The tool itself allows storyline views per country (spatial-temporal set)
  • This pic shows many days together. The tool itself allows storyline views per day (spatial-temporal set)
  • Click on keyword / descriptor prompts browsing of storylines
  • Transcript

    • 1. Spatio-Temporal-Thematic Analysis of Citizen Sensor DataChallenges and Experiences
      MeenakshiNagarajan, KarthikGomadam, Amit Sheth, AjithRanabahu, RaghavaMutharaju and AshutoshJadhav
      Kno.e.sis, Wright State University
      Presented at the conference by Pablo Mendes
      Text at: http://knoesis.org/library/resource.php?id=00559
    • 2.
      • Micro-blogging platforms – Twitter, Friendfeed..
      • 3. Revolutionizing how unaltered, real-time information is disseminated and consumed
      • 4. Significant portion of data is Experiential in nature
      • 5. First-hand observations, experiences, opinions via texts, images, audio, video (Citizens as sensors)
      2
    • 6. Citizen Sensor Observations
      Are a lens into the social perception of an event in any region at any point in time
      Mumbai Terror Attacks, Iran Elections, Obama’s Health Care Reform…
      They present complementary, sometimes lagged viewpoints that evolve over time and with other external stimuli
      3
    • 7. what is being said about an event (the theme), IS AS IMPORTANT AS
      where (spatial) and when (time) it is being said
      4
    • 8. Contribution, Presentation Focus
      A Web MashUp that
      Processes textual citizen sensor observations pertaining to real-world events
      Takes three dimensions of space, time and theme into consideration
      Extracts local and global social signals/ perceptions over time
      5
    • 9. TWITRIS – System overview
      Crawling, Processing, Visualization
      6
    • 10. Twitris - System Overview
      3.
      1.
      2.
      Obtain Topically Relevant Tweets, Extract Location, Time stamp Information, Store in DB
      2. Process Tweet Contents, Store extracted metadata in DB
      3. User Visualization talks to the DB
      7
    • 11. 1. Gathering topically relevant data, extracting Location, time stamps
      Obtaining Citizen-Sensor Observations
      8
    • 12. Crawling Tweets Relevant to Event
      • Twitter has no explicit topic categorization
      • 13. Community generated hashtags are the strongest cues
      • 14. Strategy
      • 15. Start with manually selected keywords (seed)
      • 16. Obtain additional hints from Google Insights
      • 17. Crawl using keywords, hashtags
      9
    • 18. Crawling Tweets Relevant to Event
      • Events change, Topics of discussions change
      • 19. Periodically update keywords used for crawl
      • 20. Process crawled tweets, extract top 1 TFIDF keyword, obtain Google Insights Suggestions
      • 21. Continue crawl
      10
    • 22. Challenges, Limitations of Crawl
      Volume and Rapidity of Change, Quality of data is key
      • Keyword gathering, Crawl requires supervision
      Twitter API restrictions
      • Hourly / Daily access limits
      • 23. Severe limitations on extracting past data
      • 24. Can go back only a few weeks!
      Large scale geo-code conversions
      11
    • 25. Tweet Spatio-Temporal Coordinates
      Time Stamp obtained at crawl
      Location extraction is non-trivial
      Twitter does not store location data
      Approximate tweet location using user’s location information
      Note : Recently, Twitter added per tweet geo locations
      Geocodes found for a user’s location in profile
      Use Google / Yahoo geo-coder services
      Not always provided, not always decoded to a location
      12
    • 26. Twitris - System Overview
      2.
      2. Process Tweet Contents, Store extracted metadata in DB
      13
      Extracted time stamps, geo-coordinates stored in the DB
    • 27. Spatio-Temporal Sets of Tweets
      Intuition behind processing of tweets
      Events have inherent spatial temporal biases associated with them
      Bias dictates granularity of data processing
      Mumbai terror attack: country level activity everyday
      Health care reform: US state level activity per week
      14
    • 28. Spatial Temporal Sets
      Group observations using spatial, temporal bias cues
      E.g., for Mumbai Terror Attack, create X sets of tweets per day, each cluster represents activity in one country
      Thematic processing over each set
      Ensures local, temporal social signals are preserved
      15
    • 29. Processing Tweets
      Extracting important event descriptors
      Key words or phrases (n-grams)
      What is a region paying attention to today?
      Objective: from volumes of tweets to key descriptors
      Using three attributes: Thematic, Temporal and Spatial importance of a descriptor
      16
    • 30. Descriptor: Thematic Importance
      TFIDF weighted 3-grams
      Amplified if nouns, no stop words
      Amplified by presence of contextual evidence
      GM
      GENERAL MOTORS
      BIG THREE
      CHRYSLER
      BIG 3
      FORD
      17
    • 31. Contextual Evidence
      Thematic score of a descriptor (focus word fw) is enhanced proportional to
      PMI based association strength with and thematic score of co-occurring descriptors (awi)
      Co-occurring descriptors obtained from the same spatio-temporal set as the descriptor
      18
    • 32. Certain descriptors always dominate observations
      Terrorism in the Mumbai Terror Attack
      Healthcare in the Health Care Reform discussions
      To allow less popular, interesting descriptors to surface, we discount thematic score proportional to recent popularity
      Descriptor: Thematic-Temporal Importance
      19
    • 33. Descriptors that occur all over the world not as interesting as those local to a region
      Discount thematic-temporal score proportional to number of spatial sets (not local) that mention the descriptor
      Descriptor: Thematic-Temporal-SpatialImportance
      20
    • 34. TFIDF vs. Spatio-Temporal-Thematic (STT) Scores of Descriptors
      Interesting descriptors surface up!
      Other examples in the paper
      21
    • 35. Examples of STT scored Descriptors over 5 days
      Mumbai Terror Attack
      22
    • 36. Discussions around Descriptors
      For some context : Extracting chatter surrounding a descriptor of interest
      Using a clustering approach
      Figure showing top X STT weighted descriptors
      What are people
      saying about a
      descriptor?
      (user click driven)
      23
    • 37. Clustering Algorithm Overview
      For a descriptor of interest
      We generate complementary viewpoints expressed in the data
      Using a Information Content based Clustering Algorithm
      Basic Intuition
      Among descriptor associations, select complementary viewpoint hints
      Initialize clusters with descriptor of interest, viewpoint indicator
      Expand cluster to add strong associations
      24
    • 38. Algorithm Overview – Example for focus word ‘Pakistan’
      25
    • 39. Discussions around Descriptors - Example
      Around Pakistan on a particular day
      US (shades of blue), India (orange), Pakistan (red)
      Size indicates STT score
      This summarized viz. only for presentation
      26
    • 40. Discussions around Descriptors - Example
      Around G20 in Denmark across 4 days (color)
      Size indicates STT scores
      27
    • 41. User Interface and Visualizations
      Browsing the when, what and where slices of social perceptions behind events
      28
    • 42. Data Processed – AJ FIX
      • As of March 2009
      Breakdown of crawled Tweets
      Percentage of Tweets by region
      for Financial Crisis event
      29
    • 43. Events in Twitris
      WISE 2009
      Mumbai Terror Attack, G20
      ISWC Challenge 2009
      Health Care Reform, Iran Election
      New features
      Integration with news, Wikipedia, Tweets mentioning descriptors
      Current Explorations, Investigations :
      Automated Crawl, Progressive TFIDF
      Streaming data analysis
      30
    • 44. For more information
      meena@knoesis.org
      karthik@knoesis.org
      amit@knoesis.org
      ajith@knoesis.org
      Try it on-line: http://twitris.knoesis.org
      http://knoesis.org/research/semweb/projects/socialmedia/
      31
    • 45. Development Team
      32