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

222 MILLION FACEBOOK USERS 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

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 Image Metadata latitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E Geocoder (Reverse Geo- coding) 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 A. Sheth, "A Playground for Mobile Sensors, Human Computing, and Semantic Analytics", IEEE Internet Computing, July/August 2009, pp 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).