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Analysis and Monetization of Social Data

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Analysis and Monetization of Social Data

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  1. Analysis and Monetization of Social Data • Amit P. Sheth • Lexis-Nexis Ohio Eminent Scholar • Director, Kno.e.sis Center, Wright State University

  2. 222 MILLION FACEBOOK USERS 4000000 twitter users 52,000 F8 APPLICATIONS AND COUNTING 3 Million tweets a day

  3. Intents in User Activity Elsewhere June 01, 2009

  4. What why and how people write • Cultural Entities • Word Usages in self-presentation • Slang sentiments • Intentions

  5. 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

  6. Identifying Monetizable Intents • 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’ (informationseeking) 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.

  7. 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

  8. Off topic noise – topical keywords • Google AdSense ads for user post vs. extracted topical keywords

  9. 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

  10. and then there is • space (where) • time (when) • theme (what)

  11. twitris: spatio-temporal integration of twitter data “surrounding” an event • http://twitris.dooduh.com

  12. 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?

  13. Geocoder (Reverse Geo-coding) Address to location database 18 Hormusji Street, Colaba Vasant Vihar Image Metadata latitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E Structured Meta Extraction Nariman House Income Tax Office Identify and extract information from tweets Spatio-Temporal Analysis

  14. domain models to enhance thematic • relationships

  15. who creates?

  16. I will, you will, WE will

  17. 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/