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Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

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Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, "Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications," Tutorial at WWW2011, Hyderabad, India, March 28, 2011. …

Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, "Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications," Tutorial at WWW2011, Hyderabad, India, March 28, 2011.

More info at:
http://knoesis.org/library/resource.php?id=1030

http://www2011india.com/tutorialstr27.html

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  • Got carried away with coverage and content – too much material for 3 hours – so the remaining content can be used as background
  • Got carried away with coverage and content – too much material for 3 hours – so the remaining content can be used as background
  • - We want to understand meaningful citizen sensor observation  social signals
  • Source for Stats
  • Many media companies use Facebook and Twitter as news-delivery platform. Many individuals rely on them as news source. News is increasingly social.
  • tweetmeme_url = 'http://www.readwriteweb.com/archives/this_is_what_a_tweet_looks_like.php'; tweetmeme_source = 'rww'; A tweet is filled with metadata - information about when it was sent, by who, using what Twitter application and so on
  • “isn't much of life's meaning found in the play between limits and the infinite?”
  • Link to media files, context about annotation, a special option to write reviews of movies, books, or links you're sharing. The ISBN of the book, a link to a preview of the movie and the number of stars in your rating could be included in the Tweet Annotations, Any way you can classify, describe, append or otherwise enrich a Tweet with words or numbers can be included in Annotations.
  • Interest level:(Based on Description info, lists and fav. tweets)
  • Semantic metadata, relationships: Inferred?
  • Structure Level MetadataCommunity Size - Showing scale: global vs. localCommunity growth rate - Popularity estimation for a topicLargest Strongly Connected Component size - Measuring Reachability in the directed graphNo. of Weakly Connected Components & Max. size - distribution of pre-existing network connections (follower-followee) - Showing Nature: loose vs. compactAverage Degree of Separation - How many hops between two authorsClustering Coefficient - Showing the likelihood of associationRelationship Level MetadataType of Relationship- topic/content (based on Retweet, Entity etc.)- follower/followee (based on structure)Relationship strength- Strong vs. Weak ties based on activity/ communication between users - % tie strengthUser Homophily [Homophily (i.e., "love of the same") is the tendency of individuals to associate and bond with similar others] based on certain characteristic (e.g., Location, interest etc.)% of users showing similar behaviorReciprocity: mutual relationship- % of users following back their followersActive Community/ Ties- How active is the communication between users or how active are the relationship ties - Average of tie strength based on activity
  • Pat Hayes
  • Add some examples of how people store such semantic metadata…When you put social data as LOD, talk about technologies -
  • Building on foundations of Statistical Natural Language ProcessingInformation ExtractionSemantic Web/ Knowledge RepresentationWe will talk about key issues in extracting metadata from Informal Text and how it varies from what has been done in more well-structured text like news articles etc.
  • Social Media text is informal for various reasons.. Read red points
  • Recently two researchers came up with a score to formalize the contextual nature of text and therefore the formality of text. More the available context, more formal the textWe used the same score on SM text and found that …---Score is too limited and probably outdated– does not consider full sentences/structure, does not consider links– similarly network related score would be good to have
  • What the two tasks look like in terms of outputs they produce
  • For two types of NE movie and music over two types of SM textUsing those cues
  • Focus only on one row at a time Cultural entity defn in next slide
  • What makes cultural entity extraction more difficult
  • There are two flavors to the Cultural entity recognition problemWhere same entity appears in multiple senses in the same domainWhere same entity appears in multiple senses in different domains
  • Focusing on the first flavor
  • Same song occurs as multiple instances in Music Brainz (knowledge base)
  • Sample real world constraints hard-coded – this work was an experiment into scoping using real-world constraints
  • As you chop away the domain model you accuracy increases…
  • This is an application of the NER work
  • Conclusion in RED
  • We have come a long way but still room for improvement
  • Fact can be proven, opinion cannot. An opinion is normally a subjective statement that bases on people's thoughts, feelings and understandings.
  • Social media serves as a platform for people to speak their mind more freely, which lead to a growing volume of opinionated data that can be used by:  (1) individuals for suggestion and recommendation(2) companies and organizations for marketing strategies and other decision making process(3) government for monitoring social phenomenons, being aware of potential dangerous situations, etc.
  • For the task of classification, supervised learning or unsupervised learning techniques can be used. For the review-like data, e.g., movie review or product review, it's easy to get training data from website like imdb or Amazon. The sentiment classification is different from traditional topic classification since they have different features involved.lexicon-based approach: first creating a sentiment lexicon, and then determining the polarity of a text via some function based on the positive and negative clues within the text, as determined by the lexicon. The idea of bootstrapping is to use the output of an available initial classifier to create labeled data, to which a supervised learning algorithm may be applied. The task of extracting the opinion/holder/target is similar to the traditional information extraction task. The difference is that for this task, the relations between opinion and opinion target are considered important.E.g. proximity, the opinion expression is assumed to be closed to the opinion target in the text. Based on this assumption, if the opinion target is given, then the nearby adjectives can be extracted as opinion candidates.Other possible ways to model the relations between opinion and opinion target include: syntactic dependency, co-occurrence, or manually prepared patterns/rules 
  •  In this paper, The authors connect measures of public opinion measured from polls with sentiment measured from tweets. They find that a relatively simple sentiment detector based on Tweets replicates consumer confidence and presidential job approval polls.The results highlight the potential of text streams as a substitute and supplement for traditional polling. Positive and negative words are defined by the subjectivity lexicon from OpinionFinder,a word list containing about 1,600 and 1,200 words marked as positive and negative, respectively (Wilson, Wiebe, and Hoffmann, 2005)A message is defined as positive if it contains any positive word, and negative if it contains any negative word. (This allows for messages to be both positive and negative.)
  • In this paper, the authors demonstrate how social mediacontent can be used to predict real-world outcomes. In particular, they use tweets to forecast box-officerevenues for movies. The results show that the prediction model using the rate at which tweets are created about a movie outperforms the market-based methods. And the sentiments present in tweets about a movie can be used to improve the prediction. The intuition is that a movie that has far more positive than negative tweets is likely to be successful. For the task of sentiment classification of tweets, they use a supervised classifier "DynamicLMClassifier" from LingPipe.Each tweet in the training set is labeled as positive, negative or neutral by workers from Amazon Mechanical Turk.The classifier is trained using the n-gram model. In their work, they use n=8.  they find that the sentiments do provide improvements, although they are not as important as the rate of tweets themselves 
  • One of the most attractive advantages of unsupervised approaches is that they do not require for training data.Many sentiment analysis applications for social media content use simple lexicon-based method. However, for the problem of target-specific sentiment analysis, it doesn't work. Based on simple lexicon-based method which use a general sentiment lexicon containing positive/negative/neutral words in the general sense, (1) for the task of "find tweets containing positive opinions about a specific topic", such as a movie, the results will like the table shows. However, 2,3,5,6,7 don't contain opinions about the movie. (2) for the task of extract the opinion clues/expressions, the right answers should be like we show in the other picture. However, the simple  lexicon-based method might give all the words with orange color in the table.
  • We create a general subjective lexicon which contains subjective words in the general sense. This lexicon is created by extending the commonly used subjective lexicon to involve slangs learned from Urban Dictionary.This general lexicon is used for select sentiment units candidates. A bootstrapping method is used to learn domain-dependent sentiment clues from domain-specific corpus. Most of the current lexicons only contain words. We employ statistical models to find words, phrases and patterns which can be used as sentiment clues. Such as "must see", "want my money/time back", "don't miss it" in the movie domain.For the task of identifying opinions towards the given target, we use a syntactic rule-based method as well as proximity model. Since the informal language structures of tweets bring difficulties to the parser, our method just requires a very shallow syntactic parse of tweets.
  • Refs:http://en.wikipedia.org/wiki/Writing_stylehttp://en.wikipedia.org/wiki/Psychometrics
  • Metadata from Network Analysis:- Not sufficient to answer the above questions unless we consider context, and hence merge approach (Content + Network) is better
  • [Example scenario:- Buyer wants to buy a movie dvdMultiple influencers!!!- Key Influencers: Media experts- Peer Influencers: Hiscollegues (the people, buyer interacts face-to-face daily)- Social Influencers: His social circle ]Now how do we find out them?Link Analysis based on structure is not just sufficient ---- SOCIAL MEDIA IS HIGLY COMPLEXThat’s why we need additional context analysis in play- Popularity NOT =Influence! - We need to understand audience, their activity level and interest is of greater importanceHomophily (tendency to follow similar behavior) limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience.KLOUT: (http://klout.com/kscore)Reach :: Are your tweets interesting and informative enough to build an audience? How far has your content been spread across Twitter?Amplification:: Probability is the likelihood that your content will be acted upon
  • Multiple types of users - HOW DO WE FIND OUT THESE TYPES?Does external web (background knowledge) presence of a user tells us more than the limited context available in the network?
  • User engagement levels: applications in coordination activities Connecting the dots here with NGO initiatives (*presented by Selvam)
  • User engagement levels: applications in coordination activities Connecting the dots here with NGO initiatives (*presented by Selvam)- Just not limit to Active vs. Passive in general but be specific to topic and then say ‘active’/passive w.r.t. topic (e.g., active for ‘Biology info’ vs. passive for ‘comp. sci. info.’)
  • Connections/Relationships- Implicit content features
  • We want to achieve by Network Analysis for social media: - Graph Traversal --- for understanding reachability between people - Community Formation, sustainability for people
  • We want to analyze these Social networks for understanding various social science studies:- DiffusionHomophily (tendency to follow similar behavior) – based on certain characteristic (demographic, interest etc.) What makes dynamics to be diff. here (factors)
  • Authoritativenature of the poster or the volume of follower connections did not predict the re-tweet behavior associated with the tweets!
  • Spammers diverting their attention to social media sites.
  • This tweet was by Kenneth Cole at the time of Egypt Revolution. Though it uses a hashtag that was used to indicate a tweet on Egypt crisis (#Cairo), the link it has is not connected to Egypt crisis.
  • This Article was published on Guardian website in Feb 2010. In this article the Director of BBC Peter Horrocks states that the journalists should use social media as the primary source of Information. He took over the position of Director a week back.Now let us consider a scenario where a Journalist wants to follow social signals wants to analyze what news is stirring up today at a particulat location.There is a problem using this since there is Information Overload
  • This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  • How news articles We collected the output of our system for healthcare topic for a time period. We also collected articles from the Nytimes for the same period and put this as the input for our Extraction pipeline. And plotted the occurrence of entities in tweets and in Nytimes articles. We found that both these co-ordinate very well. We then got the events occurred during the peaks of our time plot from timeline.com and nytimes.com and found this result.

Transcript

  • 1. Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web ApplicationsTutorial at WWW2011, Hyderabad, IndiaMarch 28, 2011
    1
  • 2. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    2
    Outline
  • 3. Acknowledgements
    3
  • 4. Selvam Velmurugan
    (Kiirti, eMoksha NGOs)
    Meena Nagarajan
    (Content Analysis)
    Hemant Purohit
    (People & Network analysis)
    AmitSheth
    (Semantic Web)
    Ashutosh Jadhav
    (Event Analysis)
    Lu Chen
    (Sentiment Analysis)
    Pramod Anantharam
    (Social & Sensor web)
    Pavan Kapanipathi
    (Real Time Web)
  • 5. Preliminaries
    Tutorial description: http://www2011india.com/tutorialstr27.html and http://knoesis.org/library/resource.php?id=1030
    Lots of breadth: many examples, some depth: few algorithms, mainly to convey insights
    Twitter > Myspace/Facebook > SMS
    Each has different reach/focus/importance
    Given the time, only parts will be covered today! Citations, further reading at bottom and at the end
    Images belong to their copyright holders. Copyright info. for images, where available are at the end.
    5
  • 6. Aim
    What are research opportunities and technical challenges in gaining insights and use of social media content (esp. citizen sensing)?
    Provide a structure to a vast array of issues
    Breath, not depth
    6
  • 7. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    7
    Outline
  • 8. Citizen Sensing
    Common person (citizens of Internet) is able to use Web2.0 and social networks
    The human centric activity** of observing, reporting, disseminating information (facts, opinions, views) via text, audio, video and built in device sensor (and smart devices)
    ** direct/indirect, collective/individual
    Human-in-the-loop (participatory) sensing
    + Web 2.0 + Mobile computing
    = Emergence of 
    Citizen-Sensor networks
    Image: http://bit.ly/hmZe428
    A. Sheth, 'Citizen Sensing, Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.
  • 9. Understanding meaningful citizen sensor observations
    Social Signal Processing: Aggregation, Enhancement, Analysis, Visualization, and Interpretation
    Citizen-Sensor network:
    Immense potential to disseminate social signals quickly and in real-time
    9
    Social Signals
    A. Sheth, 'Citizen Sensing, Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.
    Image:http://bit.ly/gWHSjD
  • 10.
    • Mobile Platforms Hit Critical Mass, Over 5 billions users
    • 11. 1+B with internet connected mobile devices (2010)
    • 12. Smartphones> Notebooks + Netbooks (2010E)
    • 13. 500K+ mobile phone applications
    • 14. 74% of mobile phone users (2.4B) worldwide used SMS (2007)
    Mobile device might qualify as humankind's primary tool
    Redefines the way we engage with people, information, etc.
    Enablers: Mobile Devices
    & Ubiquitous Connectivity
    Mobile is Global
    Ubiquity, 24x7
    Built in sensors
    environmental, biometric/biomedical,...
    10
  • 15. Enablers: Web 2.0 & Social Media
    500M+ Facebook Users
    100M+ Twitter users, 85M+ tweets/day
    Internet Users: 1.8 Bln
    Large variety of social media and traditional media interact, creating potent mixture
    11
    Types of UGC:
    • Twitter (text/microblogs)
    • 16. Facebook (multimedia)
    • 17. YouTube (videos)
    • 18. Flicker (images)
    • 19. Blogs (text)
    • 20. Ping(Social network for music) 
    Image: http://bit.ly/euLETT
  • 21. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media (important classes of applications)
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    12
    Outline
  • 22. Citizen Sensors in Action
    Mumbai Terror Attack
    Iran Election 2009
    Haiti Earthquake 2010
    US Healthcare Debate 2009
    13
    Image: http://huff.to/hp0OhA
  • 23. Revolution 2.0
    Political/Social Activism
    Ghonim, who has been a figurehead for the movement against the Egyptian government, told Blitzer “If you want to liberate a government, give them the internet.”
    • When Blitzer asked “Tunisia, then Egypt, what’s next?,”
    Ghonim replied succinctly “Ask Facebook.”
    http://cnn.com/video/?/video/world/2011/02/13/nr.social.media.revolution.cnn
    http://cnn.com/video/?/video/tech/2011/02/11/barnett.egypt.social.media.cnn
    Egyptian anti-government demonstrator sleeps on the pavement under spray paint that reads 'Al-Jazeera' and 'Facebook' at Cairo's Tahrir square on February 7, 2011.
    http://www.cbsnews.com/stories/2011/02/15/eveningnews/main20032118.shtml
    14
  • 24. Citizen Journalism
    15
    Twitter Journalism
    Images: http://bit.ly/9GVfPQ,
    http://bit.ly/hmrTYV
  • 25. News is increasingly Social
    Social News
    Social Media and Global Media are inter-twined.
    16
  • 26. Business Intelligence:
    Trend Spotting, Forecasting, Brand Tracking, Targeted Advertising
    Sysomos(http://www.sysomos.com/)  - Business intelligence by engaging, measuring and understanding activities in Social Media
    Trendspotting(http://trendspotting.com)
    - Detecting, analyzingandevaluating trends for business.
    Simplify(http://simplify360.com/)
    - A collaborativeplatform to monitor, measureandengage
    customersusing Social Media.
    Shoutlet(http://www.shoutlet.com/)
    - Managing social media marketing communication using a single
    platform.
    Reputation.com(http://www.reputationdefender.com/)
    - Preserves privacyanddefendsreputationbyprotectingattacks
    onpersonalinformation.
    Image: http://bit.ly/eAebBb
    17
  • 27. Social Development
    (Education, Health, eGov)
    LiveMocha (http://www.livemocha.com/)
    Online Language learning tool with social engagement 
             - bridging the gap!!
    Soliya(http://www.soliya.net/)
    Dialogue between students from diverse backgrounds
    across the globe using latest multimedia technologies
    ProjectEinstein 
    (http://digital-democracy.org/what-we-do/programs/)
    A photography-based digital penpal program connecting youths in refugee camps to the world
    PatientsLikeMe
     (http://mashable.com/2010/07/13/social-media-health-trends/)
    -   Facilitates sharing of health profiles, finding patients with similar
    ailments, and learn from discussions.
    TrialX(http://trialx.com/)
    - Finding clinical trials of new treatments and connecting with clinical trial investigators.
    18
    Image: http://bit.ly/ayyjlU
  • 28. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    19
    Outline
  • 29. Collaboration
    We “simply do not have enough genes to program the brain fully in advance,” we must work together, extending and supporting our own intelligence with “social prosthetic” systems that make up for our missing cognitive and emotional capacities: “Evolution has allowed our brains to be configured during development so that we are ‘plug compatible’ with other humans, so that others can help us extend ourselves.”
    - Harvard "Group Brain Project"
    20
  • 30. Beginnings
    Open Source 
    Linux, Apache
    Social Networks
    FaceBook, Twitter, MySpace
    Crowd Sourcing
    Wikipedia, Kiva, Ushahidi, Kiirti, SwiftRiver, Sahana
    Collaborative Governance
    Peer-to-Patent, e-Demogracia
    21
  • 31. Popular Initiatives
    FaceBook + Twitter
    Iran post-election protests
    Tunisia and Egypt uprisings  
    Ushahidi
    Kenyan post-election violence
    India, Lebanon, Afghanistan, and Sudan elections
    Haiti Earthquake
    Pakistan Floods
    Kiirti
    BBMP election monitoring
    Bangalore AutoWatch 
    22
  • 32. FixOurCity - Chennai
    Built on top of FixMyCity open-source codebase
    Stage I
     Report by Area/Ward and Street
     Integration with Google Map
    Displays Ward member name/contact details
    Select category of issue, description and severity
    Confirmation through email to avoid misuse
    Stage II/III
     Normalize incoming reports to official wards and categories
    Integration with Corporation website to allow auto-forwarding and updating of reports
    23
  • 33. Ushahidi
    Information Collection: SMS (FrontlineSMS, Clickatell),
    Email, Web
    Visualization/Interactive Mapping: Timeline, Category,
    Geo-spatial
    Alerts: Geo-spatial
    Admin: User Management, Report Moderation / Creation,
    Site Statistics
    24
  • 34. SwiftRiver
    Filtering and verification of real-time data from channels like Twitter, SMS, Email and RSS feeds. Offers organizations an easy way to apply semantic analysis and verification algorithms to different sources of information.
    Speed up the process of managing real-time data streams (email, web, sms, twitter)
    Add elusive context (location, historical data) and history (reputation of sources) to online research
    Offer a dashboard for monitoring multiple channels of information
    Offer advanced aggregation and analytic tools on or offline
    Give the user control over advance curation tools and filter
    25
  • 35. SwiftRiver Architecture - I
    26
  • 36. SwiftRiver Architecture - II
    27
  • 37. Free and Open Source Disaster Management system. A web based collaboration tool that addresses the common coordination problems during a disaster between Government, the civil society (NGOs) and the victims themselves.
    Sahana
  • 38. Mapping - Situation Awareness & Geospatial Analysis.
    Messaging - Sends & Receives Alerts via Email & SMS.
    Document Library - A library of digital resources, such as Photos & Office documents.
    Missing Persons Registry: Report and Search for Missing Persons.
    Disaster Victim Identification
    Requests Management: Tracks requests for aid and matches them against donors who have pledged aid.
    Shelter Registry - Tracks the location, distribution, capacity and breakdown of victims in Shelters
    Hospital Management System - Hospitals can share information on resources & needs.
    Organization Registry - "Who is doing What & Where". Allows relief agencies to coordinate their activities.
    Ticketing - Master Message Log to process incoming reports & requests.
    Delphi Decision Maker - Supports the decision making of large groups of Experts
    Sahana
  • 39. Peer to Patent
    Peer To Patent opens the patent examination process to public participation for the first time. It is an online system that aims to improve the quality of issued patents by enabling the public to supply the USPTO with information relevant to assessing the claims of pending patent applications.
    30
  • 40. http://www.peertopatent.org/video/p2p640/VideoPlayer.html
    31
    Peer to Patent - Video
  • 41. Kiirti
    Allows you to set up your own instance of the Ushahidi Platform without having to install it on your own web server.
    Provides pre-integrated Voice and SMS reporting capabilities within India.
    32
  • 42. 33
    Kiirti – Home Page
  • 43. 34
    Kiirti – User Interaction Flow
  • 44. Kiirti - Flywheel of Engagement
    35
  • 45. Future Possibilities
    Online Dispute Resolution
    30M+ pending cases in India's courts
    Public Policy Reviews
    Crisis Management
    Effective Local Governance
    36
  • 46. Challenges
    Challenges
    Information overload
    Processing and de-duping messages
    Accessibility (e.x. network congestion, access points, …)
    Incorrect or partial data
    Trustworthiness of source (e.x. influence, reputation, …)
    Metadata extraction (e.x. geo data, name-entity, sentiment/opinion, …)
    Collaboration
    Policy discussions
    Structure or hierarchy
  • 47. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    38
    Outline
  • 48. Dimensions of Systematic Study of Social Media
    Spatio - Temporal -Thematic+
    People - Content - Network
    39
  • 49. Social Information
    Processing
    "Who says what, to whom,
    why, to what extent and with what effect?" [Laswell]
    Network: Social structure emerges from the aggregate of relationships (ties)
    People: poster identities, the active effort of accomplishing interaction
    Content : studying the content of communication 
    40
  • 50. Studying Online Human Social Dynamics
    How does the (semantics or style of) content fit into the observations made about the network?
     
    Often, the three-dimensional dynamic of people, content and link structure is what shapes the social dynamic. 
    41
    Example: how does the topic of discussion, emotional charge of a
    conversation, the presence of an expert and connections between participants; together explain information propagation in a social network?
    Image: http://bit.ly/dFzjU2
  • 51. Why People-Content-Network + Spatial-Temporal-Thematic metadata?(Example of Understanding Crisis Data)
    42
  • 52. Metadata/Annotations
    Metadata: an organized way to study
    Types
    Creation/extraction and storage
    Use
    43
    Image: http://www.biowisdom.com/tag/metadata/
  • 53. Metadata Infrastructure:
    Example for Tweet Annotation (mapped out tweet)
    44
    Image: http://rww.to/9zyoQa
  • 54. 45
    http://www.readwriteweb.com/archives what_twitter_annotations_mean.php
  • 55. 46
  • 56. `
    People Metadata:
    Variety of Self-expression Modes
    on Multiple Social Media Platforms
    Explicit information from user profiles 
    User Names, Pictures, Videos, Links, Demographic Information, Group memberships...
        
    Implicit information from user attention metadata
    Page views, Facebook 'Likes', Comments; Twitter 'Follows', Retweets, Replies.. 
    47
  • 57. People Metadata: Various Types
    Identification
    Interests
    Activity
    Network
    48
  • 58. People Metadata: Continued
    49
  • 59. People Metadata: Continued
    Web Presence:
    - User affiliations
    - KLOUT Score – influence measure (www.klout.com)
    50
  • 60. Content Metadata
    Content Independent metadata
    • date, location, author etc.
    51
    2. Content Dependent metadata
    Direct content-based metadata
    i. Explicit/Mentioned Content metadata
    • named entities in content
    ii. Implicit/Inferred Content Metadata
    • related named entities from knowledge sources
    b. Indirect content-based metadata (External metadata)
    • context inferred from URLs in content (images, links to articles, FourSquarecheckins etc.)
    V. Kashyap and A. Sheth, 'Semantic Heterogeneity in Global Information Systems: The Role of Metadata,
    Context and Ontologies,’ in Cooperative Information Systems: Current Trends and Directions, M. Papazoglou and G. Schlageter (Eds.), Academic Press, 1998, pp. 139-178.
  • 61. Content Metadata:
    Content Independent
    For Tweets
    Published date and time
    Location (where tweet was generated from)
    Tweet posting method (smart-phone, twitter.com, clients for twitter)
    Author information
    52
    • For SMS
    Publish date and time
    Location (where SMS is generated)
    Receiver (NGO, Government organization)
    carrier information (available on request)
  • 62. Content Metadata:
    Content Dependent (Tweet)
    53
    Direct Content-based Metadata
    Indirect content-based metadata (External metadata)
  • 63. Content Metadata:
    Content Dependent (SMS)
    Direct Content-based Metadata
    54
  • 64. Network Metadata
    Connections/Relationships matter! (foundation for the network)
    55
  • 65. Metadata: Creation, Extraction and Storage
    56
  • 66. Metadata Creation & Extraction
    Extracted Metadata
    Directly visible information from the user profile, tweet content & community structure
    Created Metadata
    After processing information in the user profile, content and/or network structure
    57
  • 67. An Example
    Length: 109 charactersGeneral topic: Egypt protest 
    This poor {sentiment_expression: {target: “Lara Logan”, polarity: “negative”}} woman! RT @THRCBS News‘ {entity:{type=“News Agency”}} Lara Logan {entity:{type=“Person”}} Released FromHospital {entity:{type=“Hospital”}} After Egypt {entity:{type=“Country”} Assault {topic} http://bit.ly/dKWTY0 {external_URL}
    58
  • 68. Why Semantic Web is a Standard for Social Metadata?
    Rich Snippet, open graph: RDFa - Semantic Web based social data standards
    Relationships/connections play central role (not just hyperlinks as in Web data)– so relationship as first class object is important
    Semantic Web technologies and standards provide better techniques to capture and represent metadata, relationships
    59
  • 69. Semantic Web in One Slide
    Representing Semantic Web Data
    RDF: relationships as first class object <subject, predicate,object>
    Representing Knowledge  and Agreements
    nomenclature, taxonomy, folksonomy, ontology: OWL
    Annotation: RDFa, Xlink, model reference
    Web of Data: Linked Open Data 
    Querying: SPARQL
    Rules: SWRL, RIF
    60
  • 70. How to Save and Use Metadata?
    Store metadata as data and use standard database technique
    Use filtering and clustering, summarization, statistics - implicit semantics
    61
    • Use explicit semantics and Semantic Web standardards and technologies
    • 71. Semantics = meaning
    • 72. Richer representation, support for relationships, context
    • 73. Supports use of background knowledge
    • 74. Better integration, powerful analysis
    • 75. Use of RDF data stores/LOD 
    • 76. Semantics- the implicit, the formal and the powerful
    • 77. Social metadata on the Web [H. Dacquin]
  • Metadata Creation using Content Analysis
    Building on foundations of 
    Statistical Natural Language Processing
    Information Extraction
    Semantic Web/ Knowledge Representation
    62
  • 78. Metadata Extraction from Informal Text
    63
    Meena Nagarajan,‘Understanding User-Generated Content on Social Media,’ Ph.D. Dissertation, Wright State University, 2010
  • 79. 64
    Characteristics of Text on Social Media
  • 80. The Formality of Text
    65
  • 81. Content Analysis: Typical Sub-tasks
    • Sentiment Analysis
    • 82. What opinions are people conveying via the content?
    • 83. Author Profiling
    • 84. What can we infer about the author from the content he posts?
    • 85. Context (external to content) extraction
    • 86. URL extraction, analyzing external content
    Recognize key entities mentioned in content
    Information Extraction (entity recognition, anaphora resolution, entity classification..)
    Discovery of Semantic Associations between entities
    Topic Classification, Aboutness of content 
    What is the content about?
    Intention Analysis 
    Why did they share this content?
    66
  • 87. Research Efforts, Contributions in this space..
    Examining usefulness of multiple context cues for text mining algorithms
    Compensating for informal, highly variable language, lack of context
    Using context cues: Document corpus, syntactic, structural cues, social medium, external domain knowledge…
    In this talk, highlighting sample metadata creation tasks:
    NER
    Key Phrase Extraction
    Intention
    Sentiment/Opinion Mining
    67
  • 88. Named Entity Recognition
    I loved <movie> the hangover </movie>!
    Key Phrase Extraction
    68
    Part 1: NER, Key Phrase Extraction
  • 89. Multiple Context Cues Utilized for NER in Blogs and MySpace Forums
    69
    Meena Nagarajan,‘Understanding User-Generated Content on Social Media,’ Ph.D. Dissertation, Wright State University, 2010
  • 90. 70
    Multiple Context Cues Utilized for Keyphrase Extraction from Twitter, Facebook and MySpace
    Meena Nagarajan,‘Understanding User-Generated Content on Social Media,’ Ph.D. Dissertation, Wright State University, 2010
  • 91. Focus, Impact
    We focus on techniques that exploit
    content and context aspects on social media platforms
    Our methods highlight a combination of top-down, bottom-up analysis for informal text
    Statistical NLP, ML algorithms over large corpora (bottom-up)
    Models and rich knowledge bases in a domain(top-down)
    71
  • 92. NAMED ENTITY RECOGNITION
    72
  • 93. Named Entity Recognition
    “I loved your music Yesterday!”
    Yesterday is an album
    “It was THEHANGOVER of the year..lasted forever..
    The Hangover is not a movie
    So I went to the movies..badchoice picking “GI Jane”worse now”
    GI Jane is a movie
    73
    Task of NER : Identifying and classifying tokens
  • 94. NER in prior work vs. NER for Informal Text
    74
  • 95. Cultural Named Entities
    • NER focus in this work: Cultural Named Entities
    Artifacts of Culture
    Name of a books, music albums, films, video games, etc.
    Common words in a language
    The Lord of the Rings, Lips, Crash, Up, Wanted, Today, Twilight, Dark Knight…
    75
  • 96. What makes cultural entity extraction challenging..
    Varied senses, several poorly documented
    Star Trek: movies, TV series, media franchise.. and cuisines !!
    Changing contexts with recent events
    The Dark Knight is a movie, it is also a reference to Obamaand the health care policy
    Comprehensive sense definitions, enumeration of contexts, labeled corpora for all senses .. Are Unrealistic expectationswhen building a NER system
    NER Relaxing the closed-world sense assumptions
    76
  • 97. 77
    NER in prior work
    vs. NER for Informal Text
  • 98. A Spot and Disambiguate Paradigm
    NER is generally a sequential prediction problem
    NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task (PER, LOC, ORGN entities)
    78
    • Focus of approach: Spot and Disambiguate Paradigm
    • 99. Starting off with a dictionary or list of entities we want to spot
    • 100. Spot, then disambiguate in context (natural language, domain knowledge cues)
    • 101. Binary Classification
    • 102. Is this mention of “the hangover” in a sentence referring to a movie?
    CoNLL 2003 -- http://www.cnts.ua.ac.be/conll2003/ner/
  • 103. 79
    NER in prior work
    vs. NER for Informal Text
  • 104. Cultural NER - Two Flavors
    80
  • 105. (a) Multiple Senses in the Same Domain
    81
  • 106. Algorithm Preliminaries
    Problem Definition
    – Cultural Entity Identification : Music album, tracks
    e.g. Smile (Lilly Allen), Celebration (Madonna)
    • Corpus: MySpace comments
    – Context-poor utterances
    e.g. “Happy 25th Lilly, Alfieis funny”
    82
    • Goal: Semantic Annotation of music named entities (w.r.t MusicBrainz)
    MusicBrainz Schema
  • 107. Using a Knowledge Resource for NER is not straight-forward..
    83
  • 108. Approach Overview
    Which ‘Merry Christmas’?; ‘So Good’is also a song!
    Scoped Relationship graphs
    – Using context cues from the content, webpage title, url…
    e.g. new Merry Christmas tune
    – Reduce potential entity spot size
    e.g. new albums/songs
    • Generate candidate entities
    • Spot and Disambiguate
    84
  • 109. Sample Real-world Constraints
    Which ‘Merry Christmas’?; ‘So Good’is also a song!
    Career Restrictions
    - “release your third album already..”
    Recent Album restrictions
    - “I loved your new album..”
    Artist age restrictions
    -”happy 25thrihanna, loved alfie btw..”
    etc.
    85
  • 110. 86
  • 111. Scoping via Real-world Restrictions
    87
  • 112. Scoped Entity Lists
    User comments are on MySpace artist pages
    – Contextual Restriction: Artist name
    – Assumption: no other artist/work mention
    Naive spotter has advantage of spotting all possible mentions (modulo spelling errors)
    – Generates several false positives
    “this is bad news, ill miss you MJ”
    88
  • 113. But there are also non-music mentions
    Challenge 1: Several senses in the same domain
    Scoping relationship graphs narrows possible senses
    Solves the named entity identification problem partially
    Challenge 2: Non-music mentions
    Got your new album Smile. Loved it!
    Keep your SMILE on!
    89
  • 114. Using Language Features to eliminate incorrect mentions..
    Syntactic features
    POS Tags, Typed dependencies..
    Word-level features
    Capitalization, Quotes
    Domain-level features
    90
  • 115. Supervised Learners
    91
  • 116. Hand-labeling - Fairly Subjective
    1800+ spots in MySpace user comments from artist pages
    Manual annotations for a post: “Keep your <track>SMILE<track>on!”
    valid album/track named entity (good spot)invalid named entity (bad spot)hard-to tell (inconclusive)
    4-way annotator agreements – shows that agreeing on the accuracy of a spot is hard to do even for domain experts
    – Madonna 90% agreement
    – Rihanna 84% agreement
    – Lily Allen 53% agreement (many named entities of ambiguous nature and usage)
    92
  • 117. Combining a Dictionary Spotter + NLP Analytics
    93
    Daniel Gruhl, Meena Nagarajan, Jan Pieper, Christine Robson, AmitSheth,‘Context and Domain Knowledge Enhanced Entity Spotting in Informal Text,’ The 8th International Semantic Web Conference, 2009: 260-276
  • 118. Lessons Learned - NER on Social Media Text using a Knowledge Base
    Intelligent pruning of a knowledge base goes a long way in improving precision
    Two stage approach: chaining NL learners over results of domain model based spotters
    Improves accuracy up to a further 50%
    allows the more time-intensive NLP analytics to run on less than the full set of input data
    94
  • 119. 95
    Music NER application : BBC SoundIndex (IBM Almaden)Pulse of the Online Music Populace
    Daniel Gruhl, MeenakshiNagarajan, Jan Pieper, Christine Robson, Amit Sheth: ‘Multimodal Social Intelligence in a Real-Time Dashboard System,’ special issue of the VLDB Journal on "Data Management and Mining for Social Networks and Social Media", 2010
    Project: http://www.almaden.ibm.com/cs/projects/iis/sound/
  • 120. The Vision
    http://www.almaden.ibm.com/cs/projects/iis/sound/
    96
  • 121. 97
  • 122. Several Insights
    98
    Trending popularity of artists
    Trending topics in artist pages
    Only 4% -ve sentiments, perhaps ignore the Sentiment Annotator on this data source?
    Ignoring Spam can change ordering
    of popular artists
  • 123. Predictive Power of Data
    Billboards Top 50 Singles chart during the week of Sept 22-28 ’07 vs. MySpace popularity charts.
    User study indicated 2:1 and upto 7:1 (younger age groups) preference for MySpace list.
    Challenging traditional polling methods!
    99
  • 124. KEY PHRASE EXTRACTION
    100
  • 125. Key Phrase Extraction - Example
    Key phrases extracted from prominent discussions on Twitter around the 2009 Health Care Reform debate and 2008 Mumbai Terror Attack on one day
    101
  • 126. Key Phrase Extraction from Social Media Text
    Different from Information Extraction
    Key phrase extraction does not concern itself with classification into a type
    Extracting vs. Assigning Key Phrases
    Focus: Key Phrase Extraction
    Prior work focus: extracting phrases that summarize a document -- a news article, a web page, a journal article, a book..
    Focus here: summarize multiple documents (UGC) around same event/topic of interest
    102
  • 127. Key Phrase Extraction on Social Media Content has some differences
    1. Need to preserve/isolate the social behind the social data in
    summarizing key phrases
    What is said in Egypt vs. the USA should be viewed in isolation
    2. Need to Accounting for redundancy, variability, off-topic content
    “Met up with mom for lunch, she looks lovely as ever, good genes .. Thanks Nike, I love my new Gladiators ..smooth as a feather. I burnt all the calories of Italian joy in one run.. if you are looking for good Italian food on Main, Bucais the place to go.”
    103
  • 128. Where is the Social and Cultural Logic in UGC ?
    Thematic components
    similar messages convey similar ideas
    Space, time metadata
    role of community and geography in communication
    Poster attributes
    age, gender, socio-economic status reflect similar perceptions
    ‘Social applies to data as well as metadata’
    104
  • 129. Features used in social Key Phrase extraction (common to prior efforts)
    Focus: n-grams, spatio-temporal metadata (social components)
    Syntactic Cues: In quotes, italics, bold; in document headers; phrases collocated with acronyms
    Document and Structural Cues: Two word phrases, appearing in the beginning of a document, frequency, presence in multiple similar documents etc.
    Linguistic Cues: Stemmed form of a phrase, phrases that are simple and compound nouns in sentences etc.
    105
  • 130. Key Phrase Extraction Overview
    “President Obama in trying to regain control of the health-care debate will likely shift his pitch in September”
    1-grams: President, Obama, in, trying, to, regain, ...
    2-grams: “President Obama”, “Obama in”, “in trying”, “trying to”...
    3-grams: “President Obama in”, “Obama in trying”; “in trying to”...
    106
  • 131. A descriptor is an n-gram weighted by:
    Thematic Importance
    • TFIDF, stop words, noun phrases
    • 132. Redundancy: statistically discriminatory in nature
    • 133. Variability: contextually important
    Spatial Importance (local vs. global popularity)
    Temporal Importance (always popular vs. currently trending)
    `
    107
  • 134. 108
    TF-IDF vs. Spatio-temporal-thematic scores rank phrases differently
    Foreign relations surfaces up
    M. Nagarajan et al., Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences, Tenth International Conference on Web Information Systems Engineering, Oct 5-7, 2009: 539-553
  • 135. Next task : Eliminating Off-topic Content
    Frequency based heuristics will not eliminate off-topic content that is ALSO POPULAR
    109
    Popular Key phrases “single”, “Jesus” are unrelated to Madonna’s music
    M. Nagarajan et al., Monetizing User Activity on Social Networks - Challenges and Experiences, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99
  • 136. Elimination off-topic content : Example
    • “Yeah i know this a bit off topic but the other electronics forum is dead right now. im looking for a good camcorder, somethin not to large that can record in full HD only ones so far that ive seen are sonys”
    • “CanonHV20.Great little cameras under $1000.”
    Possible relevant phrases are: ['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']
    110
  • 137. • Assume one or more seed words (from domain knowledge base) C1 -['camcorder']
    • Extracted Key words / phrasesC2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
    • Gradually expand C1 by adding phrases from C2 that are strongly associated with C1
    • Mutual Information based algorithm [WISE2009]
    111
    Eliminating off-topic content : Approach Overview
  • 138. Key Phrases & Aboutness - Evaluations
    Are the key phrases we extracted topical and good indicators of what the content is about?
    If it is, it should act as an effective index/search phrase and return relevant content
    Evaluation Application: Targeted Content Delivery
    112
  • 139. Targeted Content Delivery -Evaluations
    We took 12K posts from MySpace and Facebook Electronics forums
    Extracted Baseline phrases using Yahoo Term Extractor
    Extracted phrases using the Key phrase extraction, elimination algorithm described earlier
    Generated Targeted Content from Google AdSense
    Asked users if the delivered content matched the posts
    113
  • 140. Targeted Content for all content vs. extracted key phrases
    114
  • 141. User Studies and Results
    115
  • 142. Social Key Phrase Extraction : Impact, Contributions
    TFIDF + social contextual cues yield more useful phrases that preserve social perceptions
    Corpus + seeds from a domain knowledge base eliminate off-topic phrases effectively
    116
  • 143. INTENTION MINING
    117
  • 144. Why do people share?
    Outside of the psychological incentives, broadly, people share to Seek Information OR Share Information
    If we understand the intent behind a post, we can build systems that respond to it better
    Focus of our work: Understand intent to deliver targeted content
    Use case: Online Content-Targeted Advertisements on Social Media Platforms
    118
  • 145. Circa 2009 -Content-based Ads
    119
  • 146. Today – Content-based Ads on Profiles
    120
  • 147. What is going on here..
    • Ads are targeted on profile interests, demographic data
    • 148. But Interests on profiles do not translate to purchase intents
    – Interests are often outdated..
    – Intents are rarely stated on a profile..
    • Some profile data does seem to work
    – Example: New store openings, sales targeted at location information in a profile
    121
  • 149. But Monetizable Intents are Elsewhere, away from their profiles..
    122
  • 150. Showing clear intents on MySpace posts but no relevant ads..
    123
  • 151. Targeted Content-based Advertizing
    –Non-trivial
    –Non-policed content
    •Brand image, Unfavorable sentiments
    –People are there to network
    •User attention to ads is not guaranteed
    –Informal, casual nature of content
    •People are sharing experiences and events
    –Main message overloaded with off
    topic content
    I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(
    1Learning from Multi-topic Web Documents for Contextual Advertisement, Zhang, Y., Surendran, A. C., Platt, J. C., and Narasimhan, M.,KDD 2008
    124
  • 152. Focus: Discuss Methodology, Preliminary Results in…
    • Identifying intents behind user posts on social networks
    – Identify Content with monetization potential
    • Identifying keywords for advertizing in user-generated content
    – Considering interpersonal communication & off-topic chatter
    125
    M. Nagarajan et al., ‘Monetizing User Activity on Social Networks - Challenges and Experiences,’ 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99
  • 153. Investigations
    User studies
    – Hard to compare activity based ads to s.o.t.a
    – Impressions to Clickthroughs
    – How well are we able to identify monetizable posts
    – How targeted are ads generated using our
    keywords vs. entire user generated content
    126
    M. Nagarajan et al., ‘Monetizing User Activity on Social Networks - Challenges and Experiences,’ 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99
  • 154. Identifying Intents on SM is different from that on the Web..
    Scribe Intent not same as Web Search Intent1
    People write sentences, not keywords or phrases
    Presence of a keyword does not imply navigational / transactional intents
    – ‘am thinking of getting X’ (transactional)
    – ‘I like my new X’ (information sharing)
    – ‘what do you think about X’ (information seeking)
    Useful here would be to identify: Transactional and Information Seeking intents
    1B. J. Jansen, D. L. Booth, and A. Spink, “Determining the informational, navigational, and transactional intent of web queries,”Inf. Process. Manage., vol. 44, no. 3, 2008.
    127
  • 155. Not Focusing on the entity but Action Patterns surrounding the entity
    “where can I find a chottopspcam”
    – User post also has an entity, which is a plus but not the main target of intent identification..
    Goal is to study
    How questions are asked and
    not topic words that indicate what the question is about
    128
  • 156. Conceptual Overview Bootstrapping to learn IS patterns
    Take a set of user posts from SNSs
    Not annotated for presence or absence of any intent
    129
  • 157. Bootstrapping to learn IS patterns
    Generate a universal set of n- gram patterns; freq > f
    S = set of all 4-grams; freq > 3
    130
  • 158. Bootstrapping to learn IS patterns
    Generate set of candidate patterns from seed words
    (why,when,where,how,what)
    Sc= all 4-grams in S that extract seed words
    131
  • 159. Bootstrapping to learn IS patterns
    User picks 10 seed patterns from Sc
    Sis= ‘does anyone know how’, ‘where do I find’, ‘someone
    tell me where’…
    132
  • 160. Bootstrapping to learn IS patterns
    Gradually expand Sis by adding
    Information Seeking patterns from Sc
    133
  • 161. Bootstrapping to learn IS patterns
    For every pis in Sis generate set of filler patterns
    134
  • 162. Bootstrapping to learn IS patterns
    ‘.* anyone know how’‘ does .* know how’
    ‘does anyone .* how’ ‘does anyone know .*’
    Look for patterns in Sc
    Functional compatibility of filler
    • words used in similar semantic contexts
    Empirical support for filler
    135
  • 163. Expanding the Pattern Pool
    Functional properties / communicative functions of words
    From a subset of LIWC1
    – cognitive mechanical (e.g., if, whether, wondering, find)
    • ‘I am thinking about getting X’
    – adverbs(e.g., how, somehow, where)
    –(e.g., someone, anybody, whichever)
    • ‘Someone tell me where can I find X’
    1Linguistic Inquiry Word Count, LIWC, http://liwc.net
    136
  • 164. Example - Acquiring New Intent Patterns..
    • ‘does * know how’
    – ‘does someone know how’
    • Functional Compatibility -Impersonal pronouns
    • Empirical Support –1/3
    – ‘does somebody know how’
    • Functional Compatibility -Impersonal pronouns
    • Empirical Support –0
    • Pattern Retained
    – ‘does john know how’
    • Pattern discarded
    Sc= {‘does anyone know how’, ‘where do I find’, ‘someone tell me where’}
    • pis= `does anyone know how’
    137
  • 165. Finer Details of the Approach are in the paper..
    Iterative algorithm, single-word substitutions, functional usage and empirical support conservatively expand the intent-seeking pool of patterns..
    Infusing new patterns and seed words
    Stopping conditions
    138
    M. Nagarajan et al., Monetizing User Activity on Social Networks - Challenges and Experiences, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99
  • 166. Sample Extracted Patterns
    139
  • 167. Identifying Monetizable Posts
    Information Seeking patterns just described are generated offline
    Finding the Information seeking intent score of a post
    – Extract and compare patterns in posts with extracted information seeking patterns
    • Transactional intent score of a post
    • 168. Using LIWC ‘Money’ dictionary : 173 words and word forms indicative of transactions, e.g., trade, deal, buy, sell, worth, price etc.
    140
  • 169. Benchmarking with FB Marketplace
    Training corpus
    8000 user posts from MySpace Computers, Electronics, Gadgets forum
    • Information SeekingIntent patterns Extraction
    • 170. 309 unique new patterns, 263 unambiguous
    • Testing patterns for recall using ‘To buy’ Facebook Marketplace where all posts are information seeking
    – extracted patterns average 81 % recall
    141
  • 171. Next task: Identifying Keywords for Advertizing
    Identifying keywords in monetizable posts
    – Plethora of work in this space
    Off-topic noise removal is our focus
    I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(
    142
  • 172. Conceptual Overview (also see slides in Key Phrase elimination section)
    • Topical hints
    – C1 -['camcorder']
    • Keywords in post
    – C2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
    • Move strongly related keywords from C2 to C1 one-by-one
    – Relatedness determined using information gain
    – Using the Web as a corpus, domain independent
    143
  • 173. Example: Off-topic Chatter Elimination
    • C1 -['camcorder']
    • C2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
    • Informative words
    ['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']
    144
  • 174. Evaluations- User Study
    Keywords from 60 monetizable user posts
    – Monetizable intent, at least 3 keywords in content
    – 45 MySpace Forums, 15 Facebook Marketplace, 30 graduate students
    – 10 sets of 6 posts each
    – Each set evaluated by 3 randomly selected users
    • Monetizable intents?
    – All 60 posts voted as unambiguously information seeking in intent
    145
  • 175. Effectiveness of using topical keywords
    • Google AdSenseads for user post vs. extracted topical keywords
    146
  • 176. Instructions –User Study
    147
  • 177. Result -2X Relevant Impressions
    Users picked ads relevant to the post
    – At least 50% inter-evaluator agreement
    For the 60 posts
    – Total of 144 ad impressions
    – 17% of ads picked as relevant
    For the topical keywords
    – Total of 162 ad impressions
    – 40% of ads picked as relevant
    148
  • 178. Evaluations: Profile Ads vs. Activity Ads
    • Are ads generated from activity more interesting than those generated from user profiles?
    Gather user’s profile information
    – Interests, hobbies, TV shows.. (non-demographic information)
    • Ask them to submit a post (simulating their social media entry)
    – Looking to buy and why (induce off-topic content)
    • Generate ads from profiles, from post (keywords)
    149
  • 179. Result - 8X more interest for non-profile ads..
    • Using profile ads
    – Total of 56 ad impressions
    – 7% of ads generated interest
    • Using authored posts
    – Total of 56 ad impressions
    – 43% of ads generated interest
    • Using topical keywords from authored posts
    – Total of 59 ad impressions
    – 59% of ads generated interest
    150
  • 180. To note…
    • User studies small and preliminary, clearly suggest
    – Monetization potential in user activity
    – Improvement for Ad programs in terms of relevant impressions
    • Evaluations based on forum, marketplace
    – Verbose content
    – Status updates, notes, community and event memberships…
    – One size may not fit all
    151
  • 181. To note…
    A world between relevant impressions and click throughs
    – Objectionable content, vocabulary impedance, Ad placement, network behavior
    – In a pipeline of other community efforts
    • No profile information taken into account
    – Cannot custom send information to Google AdSense
    152
  • 182. SENTIMENT / OPINION MINING
    153
  • 183. Content Analysis: Sentiment Analysis/Opinion Mining
    Two main types of information we can learn from user-generated content: fact vs. opinion
    Much of social media text (e.g., blogs, Twitter, Facebook) is a mix of facts and opinions.  
    For example," Latest news: Mobile web services not working in #Bahrain and Internet is extremely slow #feb14{fact}... looks like they "learned" from #Egypt {opinion}"
  • 184. Sentiment Analysis: Motivation
    Why do people oppose health care reform?
    What customers complain about?
    Which movie should I see?
    155
    Image: http://bit.ly/eZtKBF
  • 185. Sentiment Analysis: Tasks
    Example:
    “How awful that many #Egypt ian artifacts are in danger of being Destroyed. What ZahiHawassmust be thinking#jan25”
    Classification:
    Overall sentiment polarity [Pang et al. 2002], [Turney 2002], etc.
    the overall polarity is positive, neutral or negative (on the document/sentence/word level)
    For the example: overall polarity is negative
    Target-specific sentiment polarity [Yi et al. 2003], [Hu et al. 2004], etc.
    The polarity toward the given target is positive, neutral or negative
    For the Example: polarity is "negative“ for the target "egyptian artifacts“; polarity is "neutral“for target "ZahiHawass"
    156
  • 186. Sentiment Analysis: Tasks
    Example:
    “How awful that many #Egypt ian artifacts are in danger of being Destroyed. What ZahiHawassmust be thinking #jan25”
    Identification & Extraction:
    opinion[Dave et al. 2003] etc.
    opinion holder [Bethard et al. 2004] etc.
    opinion target [Hu et al. 2004] etc.
    For the example: 
    opinion="awful", opinion holder="the author", target="egyptian artifacts are in danger”
    opinion="must be thinking", opinion holder="the author", target="ZahiHawass"
    157
  • 187. Sentiment Analysis: Classification
    Supervised[Pang et al. 2002] etc.
    Labeled training data: e.g., product review, movie review, etc.
    Features: e.g., term-based, part-of-speech, syntactic relations, etc.
    Learning strategies: e.g.,SVMs), Naive Bayes, ..
    Unsupervised [Turney 2002] etc.
    lexicon-based approach [Hu et al. 2004], [Ding et al. 2008] etc.
    Using a sentiment lexicon of positive/negative sentiment words
    Bootstrapping [Thelen et al. 2002] etc.
    Iteratively trains and evaluates a classifier, starting from an unannotated corpus and a few predefined seed words,
    The task of extracting the opinion/holder/target is similar to the traditional IE task. Key distinction- the relations between opinion and opinion target are considered important.
    158
  • 188. 159
    Sentiment Analysis:
    Identification & Extraction
    • Information extraction, utilizing the relations between opinion and opinion target
    • 189. Proximity[Hu et al. 2004] etc.
    • 190. extract the nearby adjectives modifying the target topic as opinion clues
    • 191. Syntactic dependency [Popescu et al. 2005] etc.
    • 192. employed language parser to compute the syntactic dependencies to extract the opinion clues with a given target topic
    • 193. Co-occurrence[Choi et al. 2009]etc.
    • 194. heuristics: the more frequently a candidate opinion target co-occurs with any opinion clues, the more likely it is the real opinion target
    • 195. Prepared patterns/rules [Kobayashi et al. 2004] etc.
    • 196. using a set of predefined extraction patterns/rules
  • Sentiment Analysis: From Tweets to polls[O’Connor et al. 2010]
    Findings:
    • A relatively simple sentiment detector based on tweets replicates consumer confidence and presidential job approval polls.
    • 197. Highlight the potential of text streams as a substitute and supplement for traditional polling.
    Connect public opinion measured from polls with sentiment measured from tweets. 
    Lexicon-based approachfor sentiment analysis of tweets
    Within topic tweets, count messages containing positive and negative words defined by the sentiment lexicon
    160
  • 198. Sentiment Analysis: Predicting the Future With Social Media [Asur et al. 2010]
    Use tweets to forecast box-office revenues for movies.
    Traina language model classifier for sentiment classification of tweets.
    Findings:
    The prediction model using the rate at which tweets are created about a movie outperforms the market-based methods.
    The sentiments present in tweets can be used to improve the prediction.
    161
  • 199. Sentiment Analysis: Target-specific Opinion Identification & Classification of Tweets-Unsupervised Approach [kno.e.sis ongoing work]
    Simple lexicon-based method doesn't work well.
    Target of “sexy” is “Helena”
    Target of “terrific” is “reviews”
    “free” is not opinionated in
    movie domain.
    Target of “loving” is “telling”
    “well” in “as well” is not
    opinionated
    Observations:
    The opinion clues may not be toward the given target (1,2,3,6)
    The opinion clues are domain and context dependent (5,7)
    Single words are not enough (4,7,8)
    162
  • 200. Domain and context-aware sentiment lexicon generation (here take the movie domain as example)
    General subjective lexicon
    Commonly used subjective lexicon + polar slangs learned from dictionary
    Select candidate opinion clues from the domain-specific corpus based on the general lexicon
    word + surrounding context
    E.g., {“free”, “free movie”, “free movie streaming”... }, {“must”, “must see”, “a must see”, “must see movie”…} , {“well”, “as well”, “well done”… }
    Identify the opinion clues and their polarity
    Utilize information from multiple sources, including the corpus, domain knowledge (e.g., freebase, imdb), general lexicon, etc.
    Bootstrapping + statistical model
    E.g., <“must”, “must see”, positive>; <“well”, “well done”, positive>
     
    Sentiment Analysis: Target-specific Opinion Identification & Classification of Tweets-Unsupervised Approach [kno.e.sis ongoing work]
    163
  • 201. 164
    Sentiment Analysis: Target-specific Opinion Identification & Classification of Tweets-Unsupervised Approach [kno.e.sis ongoing work]
    • Target-specific opinion identification/extraction
    • 202. Predefined rules
    • 203. When generating the domain and context-aware sentiment lexicon, use a set of predefined rules to select toward-target candidate opinion clues
    • 204. Syntactic dependencies
    • 205. When using the generated lexicon to extract target-specific opinion, for each pair of <target, opinion clues> in one tweet, determine whether the opinion clues is toward the target based on their syntactic dependency.
    • 206. E.g., Lovedthe King's Speech. Funny, moving...Colin Firth is so amazing. I know, you already knew that. (“amazing” won’t be extracted since nsubj(amazing, Firth) )
    • 207. We also use predefined rules and proximity for complement
  • PEOPLE ANALYSIS
    • Deriving People Metadata
    • 208. from Content Analysis
    • 209. from Network Analysis
    • 210. Merge of two approaches
    • 211. People Analysis showing use of Merger approach (Content+Network) and derived metadata
    • 212. Finding Influential Users
    • 213. Finding User Types & Affiliation
    • 214. Measuring Social Engagement
    165
  • 215. People Analysis: Extracting People Metadata
    166
  • 216. People Analysis: Using Content to Derive People Metadata
    Personality Signals
    Extrovert, agreeable, open etc
    Blogs, Style of Writing
    Loose and periodic sentence, connotation etc.
    Psychometric analysis of content
    Knowledge, abilities, attitude etc.
    Sample study: Gendered writing styles online [Ellison et al. 2006, Nagarajan et al. 2009, ICWSM etc.]
    Self-expression tends towards attempting homophily in online dating profiles, given the tendency to 'imitate and impress' in courtship
    167
    Image: http://bit.ly/JZ6eF
    Read: ‘How’ people write @Kno.e.sis
  • 217. People Analysis: Using Network to Derive People Metadata
    Interesting questions to ask:
    Who are the most popular people* in the network
    Who are the most influential people in the network
    What are the types of people in the network
    Who are the most active people in the communities
    Who are the bridges between communities in the network, etc.
    (*People may also refer to an organization)
    168
    Metadata from Network:
    • Not sufficient to answer the above questions for specific context and time, and hence merger approach (Content + Network) is better
    e.g., An Influential node in the network will be function of time and interest of his audience.
  • 218. People Analysis: Influence
    Adding Flavor of Context Analysis
    • Popularity NOT = Influence!
    • 219. For individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity.  [Romero et al. 2010]
    • 220. Interest Similarity
    • 221. Homophily causing Reciprocity on Twitter [TwitterRank, Weng et al. 2010]
    • 222. Klout Score - True Reach, Amplification [http://klout.com]
    By Link Analysis Algorithms
    Hits [Kleinberg 1999] & variants  
    PageRank[Brin et al. 1998] & variants etc..
    Links not sufficient!
    Audience size doesn’t prove influence on twitter [Million Follower Fallacy,Cha et al. 2010]
    169
    Image: http://bit.ly/9pfTO4
  • 223. People Analysis: User types & Affiliation
    Blogger, Scientist, Journalist, Artist, Trustee, Company X in  Domain Y..
        - Multiple types and affiliations!
    User interest mining
    Key Phrase Extraction followed by semantic association on user bio, tweets, lists, favorite posts
    Twitter Study [Banerjee et al. 2009]
    • Semantic analysis of profile description*
    • 224. Web Presence: Use of Web & Knowledge bases (Wikipedia, Blogs) to build context for user types
    • 225. Entity Spotting & Extraction, followed by Semantic Association and Similarity with user-type context
    170
    Image: kahunainstitute.com
    *Read Semantics driven Social Media Analysis@ Kno.e.sis
  • 226. People Analysis: Social Engagement
    171
    Imagine a crisis scenario such as Haiti (2010) or Japan (2011) Earthquake
    • emergency teams are looking for ways to help the victims
    • 227. How effectively the community of people talking about this event online, can grow to reach potential donors and people in need of resources (food, water, first aids etc.)?
    • 228. What are the best possible ways to communicate between resource providers and people in need of resources?
    • 229. How teams can coordinate well between volunteers at a victim site, to managers in organizational structure, sitting in offices?
  • People Analysis: Social Engagement
    Can we find levels of user engagement*
    Not just limiting to Passive vs. Active
    Involvement in conversation, discussion & coordination 
    Mining Linguistics patterns & Platform specific handles in the conversations
    Unsupervised learning approach [Ritter et al. 2010]
    Frequency Distribution Analysis of user activity with added Domain Knowledge
    posting, retweet, reply, mentions, lists etc. 
    172
    Image: http://www.syscomminternational.com/
    *More at Semantics driven Social Media Analysis@ Kno.e.sis
  • 230. NETWORK ANALYSIS
    - Deriving Network Metadata
    Interesting questions
    Network Analysis – Methods
    Models
    Metrics
    Network Analysis – Algorithms
    Graph Partitioning, Traversal
    Community Discovery, Evolution
    Social Network Analysis
    Diffusion
    Homophily
    Study of 3-D Dynamics (People-Content-Network)
    - Analysis & Visualization tools
    173
  • 231. Network Analysis
    “To Discover How A, Who is in Touch with B and C, 
     Is Affected by the Relation Between B & C”    
    -John Barnes
    Interesting questions to ask:
    How communitiesform around topics- growth & evolution
    What are the effectsof presence of influential participants in the communities
    What are the effectsof content nature (or sentiment, opinions) flowing in network on the community life
    What is the community structure: degree of separation and sub-communities
    174
    Foundation of network: 
    • Nodes
    • 232. Connections/Relationships
    Image: http://www.onasurveys.com/
  • 233. Network Analysis: Methods
    Network Modeling Approaches 
    Random graph model (Erdos-Renyi model)
    start with n vertices and add edges between them at random
    Small-world model
    most nodes are not neighbors of one another, but they can be reached from every other by a small number of hops or steps (Small World Phenomenon)
    Scale-free model 
    degree distribution follows a power law, i.e., frequency of degree varies as a power of its size
    175
    Image: http://www.kudosdynamics.com/
    Important Literature:     
    [Wasserman et al. 1992, Watts et al. 1998, Albert et al. 2002, Newman et al. 2006, Marin et al. 2010, Easley et al. 2010]
  • 234. Network Analysis: Methods
    176
    Network Structure metrics
    Centrality, Connected Component, Avg. Degree, Clustering Coefficient, Avg. Path Length, Bridge, Cohesion, Prestige, Reciprocity etc.
    Social Network Analysis methods
    • Centrality (Degree, Eigenvector, Betweenness, Closeness)
    • 235. Clusters (Cliques and extensions, Communities)
    Image: http://www.kudosdynamics.com/
    Important Literature:     
    [Wasserman et al. 1992, Watts et al. 1998, Albert et al. 2002, Newman et al. 2006, Marin et al. 2010, Easley et al. 2010]
  • 236. Network Analysis: Algorithms 
    Graph Partitioning & Traversal
    Goal: Best time-complexity & reachability
    Generally follows Greedy paths
    e.g., K-way multilevel Partitioning,
    Bron-Kerbosch, K-plex, K-core or N-cliques, DFS, BFS, MST
    Community Discovery, growth, evolution
    Based on relationship types (e.g., signed network), geography/location based, interest based etc.
    Generally follow cluster analysis
    e.g., Hierarchical clustering algorithms – Top-down, bottom-up
    Further Reading:
    Modularity Maximization [Newman et al. 2006]
    Algorithms comparison survey [Balakrishnan et al. 2006]
    Online Communities [Preece 2001]
    177
    "We dream in Graph and We analyze in Matrix”
    - Barry Wellman, INSNA 
  • 237. Social Network Analysis: Diffusion & Homophily
    Social Network Analysis (Interested in information flow)
    Can we predict user actions?
    Understanding dynamics is challenging!
    Why to study Diffusion
    Maximizing Spread (Opinion, Innovation, Recommendation)
    Outbreak Detection (e.g., disease)
    Diffusion Behavior
    Power Law distribution[Leskovec et al. 2007]
    Factors impacting Diffusion
    User Homophily – similar behavior tendency [McPherson et al. 2001]
    Sampling strategy [Choudhury et al. 2010], content nature[Nagarajan et al. 2010]etc.
    178
    Image: http://bit.ly/fGkIBK
  • 238. Study of 3-D Dynamics- People, Content, Network
    Intra Community Activity and connectivity
    How well connected are individual nodes (People)
    What keeps them strongly connected over time (Relationship types - Knowledge of Content)
    179
    Will the two communities coordinate well during an event- crisis or disaster?
    - Interplay between all three dimensions – P, C, N
    • Inter-Community Connectivity
    • 239. Any bridges to connect to the other community? (People)
    • 240. Any Similarity in actions with the other community (Can Content help?)
    Image: http://themelis-cuiper.com
  • 241. Study of 3-D Dynamics-People, Content, Network
    Metadata a powerful tool to explore this dynamics*
    Studies in this direction
    A Qualitative Examination of Topical Tweet and Retweet Practices [Nagarajan et al. 2010]
    How content dictates the network flow
    User-Community Engagement by Multi-faceted Features: A Case Study on Twitter [Purohit et al. 2011] [TO BE PRESENTED TOMORROW IN SoME'11]
    What factors impact user engagement in topic discussion
    180
    *Read People-Content-Network Analysis @ Kno.esis
  • 242. Graphs showing sparse (A) and dense (B) RT networks and their corresponding follower graphs for 'call for action' and 'information sharing' type of tweets
    M. Nagarajan, H. Purohit, and A. Sheth,  ’A Qualitative Examination of Topical Tweet and Retweet Practices,’ 4th Int'l AAAI Conference on Weblogs and Social Media, ICWSM 2010
    181
  • 243. Analysis & Visualization Tools
    Network WorkBench (NWB)
    Truthy
    Graph-tool
    Orange
    Pajek
    Tulip
    …. Many tools!!
    Resource:
    http://en.wikipedia.org/wiki/Social_network_analysis_software
    182
    Image:http://truthy.indiana.edu/
  • 244. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    183
    Outline
  • 245. Trustworthiness in Social Media
    Why?
    • Social media used for critical tasks
    • 246. In Disaster scenarios (e.g. Haiti earthquake, Gulf oil spill)
    • 247. For Political revolution (e.g. Egypt political crisis)
    • 248. In Political and Social policies (e.g. health care reforms) 
    What?
    • Two step process for trustworthiness
    • 249. Remove off-topic content
    • 250. (How?) Detect spam and misleading content.
    • 251. Assess data quality of on-topic content
    • 252. (How?) Trust models to assess trustworthiness of content.
    184
  • 253. Spam in Social Networks
    • Spamming is a major issue on social media
    • 254. Email spam drops while social media spam surges2
    • 255. e.g. spam on twitter
    • 256. 2% of [100 million]1 spam tweets per day!
    Graph depicting % of spam tweets
    per day on twitter against time.
    Image: http://blog.twitter.com/2010/03/state-of-twitter-spam.html
    1http://techcrunch.com/2010/09/14/twitter-seeing-90-million-tweets-per-day/
    1http://blog.twitter.com/2011/03/numbers.html
    2http://www.allspammedup.com/2011/03/email-spam-drops-as-social-media-spam-surges/
    185
  • 257. Spam in Social Networks
    Spamming on twitter:
    Gaining Popularity
    Use of popular topic related keywords (e.g. hashtags of trending topics) to propagate off topic content.
    Launching malicious attacks
    Phishing attacks, virus, malware etc.
    Misleading the masses
    Propagating false information [Mustafaraj et al. 2010]
    Astroturf campaigns during political elections1.
    1http://truthy.indiana.edu/
    186
  • 258. Spam in Social Networks
    “By incorporating the hashtag #Cairo, which was being used to share updates about
    the political protests taking place in Egypt, it appeared to many that he was using the
    widely followed hashtag to selfishly promote the company’s clothing.”
    - by Elisabeth Giammona1
    • Trending topic “abuse”
    Egypt Protests
    Image: http://lat.ms/gHHfPg
    Will topical consistency between the tweet and the URL help detect this?
    Off-topic; Spam? Intelligent marketing?
    1http://text100.com/hypertext/2011/02/what-not-to-do-with-hashtags-the-kenneth-cole-debacle/
    187
  • 259. Spam in Social Networks
    Spam Detection
    Content-based features
    Content Size and repetition, URL usage, spam words [Benevenuto et al. 2010].
    Metadata-based features
    Account information, behavior [Ratkiewicz et al. 2010].
    Network-based features
    Provenance. (e.g. content from a reliable source)
    Community voting. (e.g. RT in case of twitter).
    Author status in the network (e.g. number of followers, friends, PageRank[Brin et al. 1998], influence [Weng et al. 2010]). 
    Removal of spam doesn’t guarantee trustworthiness
    188
  • 260. Trust in Social Networks
    Reputation, Policy, Evidence, and Provenance used to derive trustworthiness [Artz et al. 2007].
    Illustrative examples of online cues used for trust assessment
    Wikipedia: article size, number of references, author, edit history, age of the article, edit frequency, etc [Dondi et al. 2006, Moturu et al. 2009].
    Product Reviews: number of helpful, very helpful ratings, author expertise, sentiments in comments received for a review, etc [Liu et al. 2008].
    189
  • 261. Trust in Social Networks
    Gleaning primitive (edge) trust
    Trust value between two nodes is quantified using numbers. E.g., [0,1] or [-1,1] or partial ordering [Thirunarayan et al. 2009, Jøsang et al. 2002, Ganeriwal et al. 2008].
    Gleaning composite (path) trust
     Propagation via chaining and aggregation (transitivity) [Golbeck et al. 2006, Sun et al. 2006]
    Some popular algorithms for trust computation 
    Eigentrust[Kamvar et al. 2003], Spreading Activation [Ziegler et al. 2004], SUNNY [Kuter et al. 2007], etc.
    190
  • 262. Trust in Social Networks
    Trust Ontology1
    Captures semantics of trust.
    Enables representation and reasoning with trust.
    Semantics of Trust specifies, for a given trustor and trustee, the following features.
    {type, value, scope, process}
    trustor
    trustee
    1P. Anantharam, C. A. Henson, K. Thirunarayan, and A. P. Sheth, 'Trust Model for Semantic Sensor and Social Networks: A Preliminary Report', National
    Aerospace & Electronics Conference (NAECON), Dayton Ohio, July 14-16th, 2010.
    191
  • 263. Trust Ontology
    • Trust Type1 - E.g., Referral Trust, Functional Trust and Non-Functional Trust.
    • 264. Referral Trust – Agent a1 trusts agent a2’s ability to recommend another agent.
    • 265. Functional Trust – Agent a1 trusts agent a2’s ability.
    • 266. Non-Functional Trust – Agent a1 distrusts agent a2’s ability.
    • 267. Trust Value - E.g., Star rating, numeric value or partial ordering.
    • 268. Trust Scope -E.g., recommendation for a Car Mechanic, Baby sitter or a movie.
    1K. Thirunarayan, Dharan K. Althuru, Cory A. Henson, and Amit P. Sheth, “A Local Qualitative Approach to Referral and Functional Trust,”
    In: Proceedings of the The 4th Indian International Conference on Artificial Intelligence (IICAI-09), pp. 574-588, December 2009
    192
  • 269. Trust Ontology
    • Trust Process - Represents the process by which the Trust Value is computed and maintained.
    Reputation – based on past behavior.
    Policy – based on explicitly stated constraints.
    Evidence – based on seeking/verifying evidence.
    Provenance – based on lineage information.
    193
  • 270. Trust Ontology
    Anna’s car is in terrible shape
    Dick is a certified mechanic
    Bob has experience with cars
    type: referral
    process: reputation
    scope: car mechanic
    value: 8
    Bob
    type: functional
    process: policy
    scope: car mechanic
    value: 10
    ASE certified
    type: non-functional
    process: reputation
    scope: car mechanic
    value: 3
    Ben
    Dick
    194
    Anna
  • 271. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    195
    Outline
  • 272. Mobile Social Computing
    Instant Discovery: Geo-tagging and location-aware services, in combination with search, have made discovery a two-way street.
    Compressed Expression: Mobile makes social networking even more compelling
    Outsourced Memory: Cloud-based servers to store all of their mobile applications and databases
    196
    • Automated Decisions: Smart apps helps to make faster decisions or even apps makes decisions for us
    • 273. Peer Power: Mobiles can create social movements based on peer influence
    http://www.technologyreview.com/business/26654/?a=f
  • 274. Mobile Social Computing (Cont.)
    Personalized Branding: advertising are rapidly becoming personalized based on individual's needs and preferences 
    Mobiles in social development becoming an integral part of development 
    Coordination in disaster situations
    Health care delivery, especially in developing countries
    Elections and other forms of political expression
    Mobile payments will be widespread
    Social Networking Accelerating Growth of Mobile
    197
  • 275. Integrating Social And Sensor Networks
    Machine sensor observations are quantitative in nature, while human observations can be both qualitative and quantitative.
    Benefits of combining observations from humans and machine sensors
    Complementary evidence.
    Corroborative evidence.
    Applications of integrating heterogeneous sensor observations
    Situation Awareness by using  human observations to interpret machine sensor observations.
    Enhancing trustworthiness using corroborative evidence.
    198
  • 276. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    199
    Outline
  • 277. Citizen Sensing @ Real-time
    200
  • 278. Real-Time Motivation
    People can’t wait for Information
    500 years ago
    Single life time
    20 years ago
    Next day or two
    Television, News papers
    Presently
    Minutes are not considered 
    fast enough
    Digital media, Social media 
    201
    Image: http://bit.ly/fg8EI3
  • 279. Real-Time Social Media
    Is Real-Time the future of Web?
    Social Media for Real-Time Web
    Disaster Management
    Ushahidi (www.ushahidi.org)
    Real-Time Markets
    RealTimeMarkets (http://www.realtimemarkets.com/)
    Brand Tracking
    Twarql (http://wiki.knoesis.org/index.php/Twarql)
    Movie reviews
    Flicktweets (www.flicktweets.com)
    202
  • 280. Scenario
    CNN - IBN
    Feb 2011
    Journalist
    203
    Scenario
    Image: http://bit.ly/hlIutz
    203
  • 281. Challenges
    Information Overload
    Number of tweets that contained the words "Egypt," "Yemen," or "Tunisia" increased more than tenfold after January 23rd: there were 122,319 tweets between January 16 and 23 containing these terms, and 1.3 million tweets between January 24 and January 30.
    Real Time
    Can we extract social signals (through analysis) as data is generated?
    http://blog.sysomos.com/2011/01/31/egyptian-crisis-twitte/
    204
  • 282. A Semantic Web Approach
    Expressive description of Information need
    Using SPARQL (Instead of traditional keyword search)
     Flexibility on the point of view
    Ability to "slice and dice" the data in several dimensions: thematic, spatial, temporal, sentiment etc..
    Streaming data with Background Knowledge
    Enables automatic evolution and serendipity
    Scalable Real-Time delivery 
    Using sparqlPuSH (SFSW'10)
    205
  • 283. Concept Feed
    206
  • 284. Architecture
    Linked Open Social Signals @ Kno.e.sis
    P. Mendes, A. Passant, P. Kapanipathi, and A. Sheth ‘Linked Open Social Signals,’ WI2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10), Toronto, Canada, Aug. 31 to Sep. 3, 2010.
    207
  • 285. Social Sensor Server
    208
  • 286. Metadata Extractions  (Social Sensor Server)
    Named Entity Recognition
    2 Million Entities from DBPedia
    Load as Trie for efficiency
    N-grams matched
    Example: Obama, Barack Obama
    URL, HashTag Extraction
    Regex extraction
    Resolution
    URL Resolution: Follows http redirects for resolution
    HashTag Resolution: Tagdef, Tagal,WTHashTag.com
    209
    • Other Metadata provided by Twitter
    • 287. User profile: User Name, Location, Time etc..
    • 288. Tweet: RT, reply etc..
  • Structured Data(Social Sensor Server)
    RDF Annotation
    Common RDF/OWL Vocabularies
    FOAF - (foaf-project.org) Friend of a Friend
    SIOC - (sioc-project.org) Semantically Interlinked Online Communities
    OPO - (online-presence.net) Online Presence Ontology
    MOAT - (moat-project.org) — Meaning Of A Tag
    210
    • A snippet of the annotation
    <http://twitter.com/ bob/statuses/123456789>
      rdf:type   sioct:MicroblogPost ;
      sioc:content  ”Fingers crossed for the upcoming #hcrvote”
      sioc:hascreator   <http://twitter.com/bob> ;
      foaf:maker    <http://example.org/bob> ;
      moat:taggedWith   dbpedia:Healthcare_reform .
    <http://twitter.com/bob> geonames:locatedIn
    Dbpedia:Ohio .
  • 289. Semantic Publisher
    211
  • 290. Semantic Publisher
    Virtuoso to filter triples
    Queries formulated by the users are stored
    SPARQL protocol over the HTTP to access rdf from the store
    Combine data from tweet with the background knowledge in the rdf store 
    212
  • 291. Application Server & Distribution Hub
    213
  • 292. Application Server & Distribution Hub
    Distribution Hub
     PUSH Model - Pubsubhubbub protocol
     Pushes the tweets to the Application Server
    Application Server
     Delivers data to the Clients
     RSS Enable Concept feeds
    214
  • 293. Use Case 4: Competitors
    Brand Tracking - Competitors
    Background Knowledge (e.g. DBpedia)
    IPhone
    HPTabletPC
    category:Wi-Fi
    @anonymized
    This Ipad is super cool. Is it better than
    category:Touchscreen
    ?category
    skos:subject
    skos:subject
    ?competitor
    skos:subject
    moat:taggedWith
    dbpedia:IPad
    ?tweet
    215
  • 294. Demonstration
    Social Media & News
    Topic - HealthCare
    1242 Articles from Nytimes
    Around 800,000 tweets
    President Obama lays out plan for Health care reform in Speech to Joint Session of Congress (10th Sept Timeline.com)
    Obama taking an active role in Health talks in pursuing his proposed overhaul of health care system. (13th Aug Nytimes)
    216
    216
  • 295. Twarql on Linked Open Data
    217
    Image: http://bit.ly/aTOV1r
    217
  • 296. Twarql (Real-Time Event Following)
    Motivation - Rapidly Evolving Events
    Example : Egypt protests, Health care debate, Iran Election
    Twarql (Real-Time Event Following)
  • 297. Adapt to the changes of the event
    219
    Approach – Continuous Semantics
    Continuous Semantics @ Kno.e.sis
    Sheth, C. Thomas, and P. Mehra, Continuous Semantics to Analyze Real-Time Data,
    IEEE Internet Computing, 14 (6), November-December 2010, pp. 84-89.
  • 298. Twarql (Real-Time Event Following) Twarql (Real-Time Event Following)
    Twarql (Real-Time Event Following)
    Twarql
    Delivers tweets in Real-Time
    Leverages background knowledge
    Constraint (Sparql – Query)
    Doozer
    Mines Wikipedia
    Automatically created domain model
    http://wiki.knoesis.org/index.php/Twarql
    http://knoesis.wright.edu/research/ModelCreation/
  • 299. Continuous Semantics(Cycle)
    Filter tweets (Twarql)
    Leverage automatically created domain models
    Extract Event descriptors (Twarql)
    From the filtered tweets.
    Create Domain models (Doozer)
    Use extracted event descriptors and Back to (1).
    Continuous Semantics(Cycle)
  • 300. Twarql(Real-Time Event Following)
    Architecture
    New addition
  • 301. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    223
    Outline
  • 302. Research Application: Twitris
    224
  • 303. Twitris - Motivation
    Image: http://bit.ly/etFezl
    1. Information Overload
    Multiple events around us
    WHAT to be aware of
    Multiple Storylines about same event!!
    225
  • 304. Twitris - Motivation
    2. Evolution of Citizen Observation
    with location and time 
    226
  • 305. Twitris - Motivation 
    3. Semantics of Social perceptions
    What is being said about an event (theme)
    Where (spatial)
    When (temporal )
    Twitrislets you browse citizen reports using social perceptions as the fulcrum
    227
  • 306. Twitris: Semantic Social Web Mash-up
    Facilitates understanding of multi-dimensional social perceptions over SMS, Tweets, multimedia Web content, electronic news media
    228
    228
  • 307. Twitris: Architecture
    229
    MeenakshiNagarajan, KarthikGomadam, AmitSheth, AjithRanabahu, RaghavaMutharaju and AshutoshJadhav, ‘Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences,’ Tenth International Conference on Web Information Systems Engineering, 539 - 553, Oct 5-7, 2009.
  • 308. Twitris: Functional Overview
    230
  • 309. Twitris: Event Summarization 1
    231
  • 310. Twitris: Event Summarization 2 
    Sentiment Analysis
    using statistical and machine learning techniques 
    232
  • 311. Twitris: Event Summarization 3
    Entity-relationship graph 
    using semantically annotated DBpedia entities mentioned in the tweets 
    233
  • 312. Twitris: Demo, Quick Show 
    http://twitris.knoesis.org/
    Many other interesting efforts – Eg: VivekK. Singh, MingyanGao, and Ramesh Jain. 2010. From microblogs to social images: event analytics for situation assessment. In Proceedings of the international conference on Multimedia information retrieval (MIR '10). ACM, New York, NY, USA, 433-436.
    234
  • 313. Citizen Sensing
    Overview, Social Signals, Enablers
    Role of Social Media
    Activism, Journalism, Business Intelligence, Global Development
    Development-Centric Platforms
    Beginnings, Architectures and Possibilities
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    Conclusion & Future Work
    235
    Outline
  • 314. Citizen Sensing
    Role of Social Media
    Development-Centric Platforms
    Systematic Study of Social Media
    Spatio-Temporal-Thematic + People-Content-Network Analysis
    Trustworthiness in Social Media
    Mobile Social Computing
    Citizen Sensing @ Real-time
    Research Application: Twitris
    236
    Conclusion & Future Work
  • 315. Twitris: Coordination
    Great role in military and NGO rescue operations during emergencies: Haiti and Chile Earthquakes
    237
  • 316. Twitris: Coordination
    Coordinating needs and resources in disaster situation
    Analyze SMS and Web reports from disaster location
    Use domain models for efficient and timely coordination
    238
    Image: http://bit.ly/hcp4PG
    238
  • 317. Twitris: CommunityFormation
    Homophily in society
    Bond of common interest
    TRUST factor
    239
  • 318. Do you have a sense of immense opportunity of analyzing citizen sensing for useful social signals?
    Do you appreciate the broad range of issues and challenges? Did we present examples and a few insights into how to address some unique challenges?
    Did spatio-temporal-thematic and people-content-network dimensions present reasonable way to organize cast number of relevant research challenges and techniques?
    Do you have more material to follow up on topics of interest?
    240
    Conclusions
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    [Anantharam et al. 2010] P. Anantharam, C. A. Henson, K. Thirunarayan, and A. P. Sheth, 'Trust Model for Semantic Sensor and Social Networks: A Preliminary Report', National Aerospace & Electronics Conference (NAECON), Dayton Ohio, July 14-16th, 2010.
    [Thirunarayan et al. 2009] K. Thirunarayan, D. K. Althuru, C. A. Henson, and A. P. Sheth, 'A Local Qualitative Approach to Referral and Functional Trust,' In: Proceedings of the The 4th Indian International Conference on Artificial Intelligence (IICAI-09), pp. 574-588, December 2009.
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  • 331. About us
    MeenaNagarajan is a research staff scientist at the IBM Almaden Research Center. Meena completed her dissertation on "Understanding User-Generated Content on Social Media" in 2010 at Wright State University Center of Excellence on Knowledge-enabled Computing (Kno.e.sis). She has collaborated with IBM Research, Microsoft Research, Marti Hearst at University of Berkeley and HP Labs. She has extensively published and served on Program Committees for key conferences. She also has the rare distinction of being invited to give a keynote (http://knoesis.org/library/resource.php?id=731), chair a panel before completing her Ph.D., and was selected for the prestigious NSF CI Fellows award. Her research has played a key role in state of the art social media and analytics systems such as BBC SoundIndex.
     
    Amit Sheth is an educator, researcher and entrepreneur. He is the LexisNexis Ohio Eminent Scholar at the Wright State University, Dayton OH. He directs Kno.e.sis - the Ohio Center of Excellence in Knowledge-enabled Computing (http://knoesis.org, http://bit.ly/coe-k) which works on topics in Semantic, Social, Sensor, and Services computing over the Web, with the goal of advancing from the information age to meaning age. Prof. Sheth is an IEEE fellow and is one of the most highly cited authors in Computer Science (h-index = 68, http://bit.ly/CS-h) and World Wide Web (http://bit.ly/mas-www). He is Editor-in-Chief of the International Journal of Semantic Web & Information Systems, joint- Editor-in-Chief of Distributed & Parallel Databases, series co-editor of two Springer book series and serves on several editorial boards. By licensing his funded university research, he has also founded and managed two successful companies. Several commercial products and many operationally deployed applications have resulted from his R&D. More about Amit: http://knoesis.org/amit
     
    SelvamVelmurugan is a technology entrepreneur, social entrepreneur and a leader/evangelist in use of social technology for development. He served in technology and leadership positions at Amazon and led the establishment of Amazon's development center in India.  Then he founded eMoksha (emoksha.org), a non-partisan non-profit organization focused on enabling citizen awareness and engagement using internet, mobile technologies and Kiirti (kiirti.org), a platform to enable effective governance by promoting awareness and citizen engagement by engaging citizens through phone, SMS, email and the web. eMoksha and Kiirti projects in developing countries are being studied by organizations around the world (including Harvard and Columbia) to  understand the role and impact of technology in enabling transparency  and civic engagement. Selvam was selected as a Center for Internet and Society (India) Fellow and was invited to attend TEDIndia in 2009. More about Selvam: http://www.ted.com/profiles/view/id/306995
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