analysis and monetization of social data amit p. sheth lexis-nexis ohio eminent scholar director,...
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Analysis and Monetization of Social DataAmit P. ShethLexis-Nexis Ohio Eminent ScholarDirector, Kno.e.sis Center, Wright State University
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222 MILL
ION
USERS 4000000 twitter users
3 Million tweets a day52,000 F8 APPLICATIONS
AND COUNTING
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Intents in User Activity Elsewhere
June 01, 2009
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What why and how people write
Cultural Entities
Word Usages in self-presentation
Slang sentiments
Intentions
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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
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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’ (information seeking)
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.
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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
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Off topic noise – topical keywords
• Google AdSense ads for user post vs. extracted topical keywords
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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
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and then there is
space (where)
time (when)
theme (what)
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twitris: spatio-temporal integration of twitter data “surrounding” an event
http://twitris.dooduh.com
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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?
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Image Metadatalatitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E
Image Metadatalatitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E
Geocoder (Reverse Geo-
coding)
Geocoder (Reverse Geo-
coding)
Address to location database
Address to location database
18 Hormusji Street, Colaba
Nariman House
Identify and extract information from tweets
Identify and extract information from tweetsSpatio-Temporal AnalysisSpatio-Temporal Analysis
Structured Meta Extraction
Structured Meta Extraction
Income Tax Office
Vasant Vihar
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domain models to enhance thematic
relationships
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who creates?
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I will, you will, WE will
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More at [email protected]: http://knoesis.orgA. 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/
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