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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. 4000000 twitter users. 52,000 F8 APPLICATIONS AND COUNTING. 3 Million tweets a day. Intents in User Activity Elsewhere.

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


4000000 twitter users


3 Million tweets a day

what why and how people write
What why and how people write
  • Cultural Entities
  • Word Usages in self-presentation
  • Slang sentiments
  • Intentions
work and preliminary results in
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
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.

from x to action patterns
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
Off topic noise – topical keywords
  • Google AdSense ads for user post vs. extracted topical keywords
8x generated interest
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
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?


(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

more at library@kno e sis http knoesis org
More at [email protected]:
  • 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).