Spatio-Temporal-Thematic Analysis of Citizen Sensor DataChallenges and Experiences<br />MeenakshiNagarajan, KarthikGomadam...
<ul><li> Micro-blogging platforms – Twitter, Friendfeed..
 Revolutionizing how unaltered, real-time information is disseminated and consumed
 Significant portion of data is Experiential in nature
First-hand observations, experiences, opinions via texts, images, audio, video (Citizens as sensors)</li></ul>2<br />
Citizen Sensor Observations<br />Are a lens into the social perception of an event in any region at any point in time <br ...
what is being said about an event (the theme), IS AS IMPORTANT AS<br />where (spatial) and when (time) it is being said<br...
Contribution, Presentation Focus<br />A Web MashUp that <br />Processes textual citizen sensor observations pertaining to ...
TWITRIS – System overview<br />Crawling, Processing, Visualization<br />6<br />
Twitris - System Overview<br />3.<br />1.<br />2.<br />Obtain Topically Relevant Tweets, Extract Location, Time stamp Info...
1. Gathering topically relevant data, extracting Location, time stamps<br />Obtaining Citizen-Sensor Observations<br />8<b...
Crawling Tweets Relevant to Event<br /><ul><li>Twitter has no explicit topic categorization
Community generated hashtags are the strongest cues
Strategy
Start with manually selected keywords (seed)
Obtain additional hints from Google Insights
Crawl using keywords, hashtags</li></ul>9<br />
Crawling Tweets Relevant to Event<br /><ul><li>Events change, Topics of discussions change
Periodically update keywords used for crawl
Process crawled tweets, extract top 1 TFIDF keyword, obtain Google Insights Suggestions
Continue crawl</li></ul>10<br />
Challenges, Limitations of Crawl<br />Volume and Rapidity of Change, Quality of data is key<br /><ul><li>Keyword gathering...
Severe limitations on extracting past data
Can go back only a few weeks!</li></ul>Large scale geo-code conversions<br />11<br />
Tweet Spatio-Temporal Coordinates<br />Time Stamp obtained at crawl<br />Location extraction is non-trivial<br />Twitter d...
Twitris - System Overview<br />2.<br />2.  Process Tweet Contents, Store extracted metadata in DB<br />13<br />Extracted t...
Spatio-Temporal Sets of Tweets<br />Intuition behind processing of tweets<br />Events have inherent spatial temporal biase...
Spatial Temporal Sets<br />Group observations using spatial, temporal bias cues<br />E.g., for Mumbai Terror Attack, creat...
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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 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 of "Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data: Challenges and Experiences"

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

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