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Monetizing User Activity on Social Networks - Challenges and Experiences 

Meena Nagarajan , Amit P. Sheth KNO.E.SIS Center Wright State University. Monetizing User Activity on Social Networks - Challenges and Experiences  .

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Monetizing User Activity on Social Networks - Challenges and Experiences 

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  1. Meena Nagarajan, Amit P. Sheth KNO.E.SIS Center Wright State University Monetizing User Activity on Social Networks - Challenges and Experiences  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, Milan, Italy

  2. Targeted Content Delivery • On social networks • Use case for this talk • Targeted content = content-based advertisements • Target = user profiles • Content-based advertisements CBAs • Well-known monetization model for online content

  3. Today - Content-based Ads May 30,June 02 2009

  4. Today - Ads on Profiles June 01, 2009

  5. What is going on here.. • Interests do not translate to purchase intents • Interests are often outdated.. • Intents are rarely stated on a profile.. • Cases that work • New store openings, sales • Highly demographic-targeted ads

  6. Intents in User Activity Elsewhere June 01, 2009

  7. Content Ads Outside Profiles CONTENT-BASED ADS ON THEIR PROFILES June 01, 2009

  8. Targeted Content-based Advertizing • Non-trivial • Non-policed content • Brand image, Unfavorable sentiments1 • People are there to network • User attention to ads is not guaranteed • Informal, casual nature of content • People are sharing experiences and events • Main message overloaded with off topic content • I NEED HELP WITH SONY VEGAS PRO 8!! Ugh and i have a video project due tomorrow for merrill lynch :(( all i need to do is simple: Extract several scenes from a clip, insert captions, transitions and thats it. really. omggi cant figure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleasssse, help? :( 1Learning from Multi-topic Web Documents for Contextual Advertisement, Zhang, Y., Surendran, A. C., Platt, J. C., and Narasimhan, M.  , KDD 2008

  9. What How Why People Write • Cultural Entities • Word Usages in self-presentation • Slang sentiments • Intentions

  10. Work and Preliminary Results in… • Identifying intents behind user posts on social networks • Content with monetization potential • Identifying keywords for advertizing in user-generated content • Interpersonal communication & off-topic chatter

  11. Investigations • User studies • Hard to compare activity based ads to s.o.t.a • Impressions to Clickthroughs • How well are we able to identify monetizable posts • How targeted are ads generated using our keywords vs. entire user generated content

  12. Intentions Behind Content Identification, Evaluation

  13. 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.

  14. 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 chottopsp cam” • User post also has an entity

  15. Conceptual Overview Bootstrapping to learn IS patterns Set of user posts from SNSs Not annotated for presence or absence of any intent

  16. Bootstrapping to learn IS patterns Generate a universal set of n-gram patterns; freq > f S = set of all 4-grams; freq > 3

  17. Bootstrapping to learn IS patterns Generate set of candidate patterns from seed words (why,when,where,how,what) Sc = all 4-grams in S that extract seed words

  18. Bootstrapping to learn IS patterns User picks 10 seed patterns from Sc Sis = ‘does anyone know how’, ‘where do i find’, ‘someone tell me where’….

  19. Bootstrapping to learn IS patterns Sc = all 4-grams in S that extract seed words Sis = ‘does anyone know how’, ‘where do i find’, ‘someone tell me where’…. Gradually expand Sis by adding Information Seeking patterns from Sc

  20. Bootstrapping to learn IS patterns Sis = ‘does anyone know how’, ‘where do i find’, ‘someone tell me where’…. For every pisinSis generate set of filler patterns

  21. Bootstrapping to learn IS patterns ‘.* anyone know how’ ‘does anyone .* how’ ‘does anyone know .*’ ‘does .* know how’ ‘does anyone know how’ • Look for patterns in Sc • Functional compatibility of filler • words used in similar semantic contexts • - Empirical support for filler

  22. Expanding the Pattern Pool • Functional properties / communicative functions of words • From a subset of LIWC1 • cognitive mechanical (e.g., if, whether, wondering, find) • ‘I am thinking about getting X’ • adverbs (e.g., how, somehow, where) • impersonalpronouns (e.g., someone, anybody, whichever) • ‘Someone tell me where can I find X’ 1Linguistic Inquiry Word Count,LIWC, http://liwc.net

  23. Extracting and Scoring Patterns • Sc= {‘does anyone know how’, ‘where do I find’, ‘someone tell me where’} • pis= `does anyone know how’ • ‘does * know how’ • ‘does someone know how’ • Functional Compatibility - Impersonal pronouns • Empirical Support – 1/3 • ‘does somebody know how’ • Functional Compatibility - Impersonal pronouns • Empirical Support – 0 • Pattern Retained • ‘does john know how’ • Pattern discarded

  24. Details in paper for.. • Over iterations, single-word substitutions, functional usage and empirical support conservatively expands Sis • Infusing new patterns and seed words • Stopping conditions

  25. Sample Extracted Patterns • does anyone know how • anyone know how to • idont know what • know where i can • tell me how to • idont know how • anyone know where i • does anyone know where • does anyone know what • anybody know how to • anyone know how i • im not sure what • does anybody know how • does anyone know why • i was wondering how • does anyone know when • tell me what to • im not sure how • i was wondering what • no idea how to • someone tell me how • have no clue what • does anyone know if • idont know if • know if i can • anyone know if i • im not sure if • i was wondering if • idea what you are • let me know how • and idont know • now idont know • but idont really • was wondering if someone • would like to see • see what i can • anyone have any idea • wondering if someone could • was wondering how i • i do not want

  26. Identifying Monetizable Posts • Information Seeking patterns generated offline • Information seeking intent score of a post • Extract and compare patterns in posts with extracted patterns • Transactional intent score of a post • LIWC ‘Money’ dictionary • 173 words and word forms indicative of transactions, e.g., trade, deal, buy, sell, worth, price etc.

  27. Benchmarking – F8 Marketplace • Training corpus • 8000 user posts • MySpace Computers, Electronics, Gadgets forum • 309 unique new patterns, 263 unambiguous • Testing patterns for recall • ‘To buy’ Marketplace – average 81 %

  28. Identifying Keywords Off-topic noise elimination

  29. Keywords for Advertizing • Identifying keywords in monetizable posts • Plethora of work in this space • Off-topic noise removal is our focus • I NEED HELP WITH SONY VEGAS PRO 8!! Ugh and i have a video project due tomorrow for merrill lynch :(( all i need to do is simple: Extract several scenes from a clip, insert captions, transitions and thats it. really. omggi cant figure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleasssse, help? :(

  30. Conceptual Overview • Topical hints • C1 - ['camcorder'] • Keywords in post • C2 - ['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic'] • Move strongly related keywords from C2 to C1 one-by-one • Relatedness determined using information gain • Using the Web as a corpus, domain independent

  31. Off-topic Chatter • C1 - ['camcorder'] • C2 - ['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic'] • Informative words • ['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']

  32. Evaluations Preliminary work

  33. Evaluations - User Study • Keywords from 60 monetizableuser posts • Monetizable intent, at least 3 keywords in content • 45 MySpace Forums, 15 Facebook Marketplace, 30 graduate students • 10 sets of 6 posts each • Each set evaluated by 3 randomly selected users • Monetizable intents? • All 60 posts voted as unambiguously information seeking in intent

  34. 1. Effectiveness of using topical keywords • Google AdSense ads for user post vs. extracted topical keywords

  35. Instructions – User Study • Choose relevant Ad Impressions • VW 6 disc CD changer • I need one thats compatible with a 2000 golf most are sold from years 1998-2004if anyone has one [or can get one] PLEASE let me know!

  36. Result - 2X Relevant Impressions • Users picked ads relevant to the post • At least 50% inter-evaluator agreement • For the 60 posts • Total of 144 ad impressions • 17% of ads picked as relevant • For the topical keywords • Total of 162 ad impressions • 40% of ads picked as relevant

  37. 2. Profile Ads vs. Activity Ads • User’s profile information • Interests, hobbies, tv shows.. • Non-demographic information • Submit a post • Looking to buy and why (induced noise) • Ads that generate interest, captured attention

  38. Result - 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

  39. To note… • User studies small and preliminary, clearly suggest • Monetization potential in user activity • Improvement for Ad programs in terms of relevant impressions • Evaluations based on forum, marketplace • Verbose content • Status updates, notes, community and event memberships… • One size may not fit all

  40. To note… • A world between relevantimpressions and clickthroughs • Objectionable content, vocabulary impedance, Ad placement, network behavior • In a pipeline of other community efforts • No profile information taken into account • Cannot custom send information to Google AdSense

  41. Assets from MSR – What would we like… • Keywords to Ad Impressions • Google Adsense like web service • Monetization potential of a keyword on the Web not the same on a social n/w? • Ranking keywords in user post • We are building an F8 app • Collaboration for clickthrough data

  42. Thank you • Google/Bing: Meena Nagarajan • meena@knoesis.org • http://knoesis.wright.edu/students/meena/ • Google/Bing: AmitSheth • amit@knoesis.org • http://knoesis.wright.edu/amit

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