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Analysis and Monetization of Social Data
 

Analysis and Monetization of Social Data

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Amit Sheth, "Analysis and Monetization of Social Data" , Panel on Semantifying Social Network, 2009 Semantic Technology Conference, San Jose, CA, June 22, 2009.

Amit Sheth, "Analysis and Monetization of Social Data" , Panel on Semantifying Social Network, 2009 Semantic Technology Conference, San Jose, CA, June 22, 2009.
http://www.semantic-conference.com/ataglance/

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  • monetizing social data, advertising on social networks, spatio-temporal-thematic analysis of social data, Twitris, analysis of user generated content
  • Social networks are growing at a rabid pace. Creating lots of data: observational data, people – people relationship data, user generated content etc
  • Using profile information - Micro targeted ads (demographic-based) -- Sponsored ads What we are seeing today: Is not apparently appealing to its members – show / tell stats that is showing that no one is paying atn to ads here What is happening here -- No intents
  • A concert – show tickets; food – red robin ; intent to purchase – body shop
  • How people write? Why they write and what they write? Only content based; there are other techniques such as link based analysis, social behavior influence
  • We understand there are other dimensions to this analysis – identifying objectionable content and unfavorable sentiments .. Here we will focus on these two problems
  • Information seeking Intent is to solicit responses concerning a question that addresses the information needs of the user. The query can be one asking for information toward the end goal of comparisons, transactions, locating a web page etc. Information sharing Intent is to inform. Users are typically sharing information or opinion about a product, an experience, promotions etc. Transactional Intent is to express an explicit buy, sell or trade intents. The goal is to seek responses that will provide cues leading to an offline (outside-network) transaction.
  • Find intent, remove off topic noise
  • Follow this with two snapshots showing temporal and spatial variations
  • What is new and interesting? What’s a region paying attention to today? What are people most excited or concerned about? Why an entity’s perception changing over time in any region?

Analysis and Monetization of Social Data Analysis and Monetization of Social Data Presentation Transcript

  • Analysis and Monetization of Social Data Panel on Semantifying Social Networks, 2009 Semantic Technology Conference , San Jose CA, June 22, 2009
    • Amit P. Sheth
    • Lexis-Nexis Ohio Eminent Scholar
    • Director, Kno.e.sis Center, Wright State University
    • Thanks: Meena Nagarajan
  • 222 MILLION FACEBOOK USERS 4000000 twitter users 3 Million tweets a day 52,000 F8 APPLICATIONS AND COUNTING
  •  
  • Intents in User Activity Elsewhere June 01, 2009
  • What why and how people write
    • Cultural Entities
    • Word Usages in self-presentation
    • Slang sentiments
    • Intentions
  • Work and Preliminary Results in…
    • Identifying intents behind user posts on social networks
      • Pull UGC with most monetization potential
    • Identifying keywords for advertizing in user-generated content
      • Interpersonal communication & off-topic chatter
  • Identifying Monetizable Intents
    • Scribe Intent not same as Web Search Intent 1
    • 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 )
    1 B. 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.
  • From X to Action Patterns
    • Action patterns surrounding an entity
    • How questions are asked and not topic words that indicate what the question is about
    • “ where can I find a chotto psp cam”
      • User post also has an entity
  • Off topic noise – topical keywords
    • Google AdSense ads for user post vs. extracted topical keywords
  • 8X Generated Interest
    • 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
    • and then there is
    • space (where)
    • time (when)
      • theme (what)
  •  
    • twitris: spatio-temporal integration of twitter data “surrounding” an event
    • http://twitris.dooduh.com
  • Studying social signals
    • What is new and interesting?
    • What’s a region paying attention to today? What are people most excited or concerned about?
    • Why an entity’s perception changing over time in any region?
  • Image Metadata latitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E Geocoder (Reverse Geo-coding) Address to location database 18 Hormusji Street, Colaba Nariman House Identify and extract information from tweets Spatio-Temporal Analysis Structured Meta Extraction Income Tax Office Vasant Vihar
  •  
    • domain models to enhance thematic
    • relationships
    • who creates?
    • I will, you will, WE will
  • More at library@Kno.e.sis: http://knoesis.org
    • A. Sheth, "A Playground for Mobile Sensors, Human Computing, and Semantic Analytics", IEEE Internet Computing, July/August 2009, pp. 80-85.
    • M. Nagarajan, K. Baid, A. P. Sheth, and S. Wang, "Monetizing User Activity on Social Networks - Challenges and Experiences“, 2009 IEEE/WIC/ACM International Conference on Web Intelligence WI-09 , Milan, Italy
    • M. Nagarajan, et al. Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences, Web Information Systems Engineering- WISE-2009 , Poznan, Poland (to appear).
    http://knoesis.org/research/semweb/projects/socialmedia/
  •