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Monetizing User Activity on Social Networks - Challenges
and Experiences
Meenakshi Nagarajan, Kamal Baid, Amit Sheth and Shaojun Wang
KNOESIS, Wright State University
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
Targeted Content Delivery on SNSs
Content-based advertisements (CBAs) Well-known monetization model on the
Web but not translating well on SNSs
Monetizing content on Web 2.0 Where to monetize What to monetize
It’s the talk of the town!
State of the art – Content-Based Ads on SNSs
May 30,June 02 2009
June 01, 2009
State of the art – Content-Based Ads on SNSs
What is going on here..
Interests stated on user home/profile pages do not translate to purchase intents Interests are often outdated.. Intents are rarely stated on a profile..
Some highly demographic targeted cases work
Overall, click through stats are staggeringly low – show some
Intents in User Activity Elsewhere.. Missed Opportunities
June 01, 2009
Concert tickets
MP3 downloads
Services in and around location
June 01, 2009
Intents in User Activity Elsewhere.. Missed Opportunities
Challenges in Monetizing User Gener
Informal, casual nature of content▪ People are sharing experiences and events▪ Main message overloaded with off topic content
Non-policed content▪ Brand image, Unfavorable sentiments1
People are there to network▪ User attention to ads is not guaranteed
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. omgg i 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
Talk Outline
System that generates ads based on activity (user generated content) elsewhere by
1. Identifying monetizable posts: intents behind user posts Pull content with monetization potential
2. Identifying keywords for advertizing from monetizable posts Dealing with off-topic chatter
System Overview and User Studies
User studies Hard to compare activity based ads to s.o.t.a
So we evaluate subgoals How well are we able to identify monetizable
posts (component 1) How targeted are ads generated using our
keywords vs. entire user generated content (component 2)
Intentions Behind ContentIdentification, Evaluation
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.
From Entities to Action Patterns
Action patterns surrounding an entity (X)
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
Bootstrapping to learn Information Seeking (IS) Patterns – offline step
MySpace User Posts (not annotated for intent)
Extract all 4-grams > freq 3
Using seed words (who, when, why, what, how)
Extract all 4-grams containing seed words
Candidate / Potential set of patterns (Sc)
‘does anyone know how’, ‘where do i find’, ‘someone tell me where’…
Bootstrapping to learn IS patterns
10 manually pickedInformation SeekingPatterns Sis‘how cool are we’ is not
Information Seeking
Remaining candidate patterns Sc = Sc - Sis
Candidate patternsSc
Goal: Evaluate candidate patterns and judge if it is Information Seeking or not
‘does anyone know how’, ‘where do i find’, ‘someone tell me where’…
Bootstrapping to learn IS patterns
‘.* anyone know how’
‘does .* know how’
‘does anyone .* how’
‘does anyone know .*’
For each fillerLook for patterns in candidate pool Sc-Functional compatibility of filler- words used in similar semantic contexts
- Empirical support for filler
‘does anyone know how’
For every known Information Seeking pattern in Sis generate set of filler patterns
Extracting and Scoring Patterns - Example
Known Information Seeking patterns Sis = {‘does anyone know how’, ‘where do I find’, ‘someone tell me where’}
pis from Sis = `does anyone know how’
Match ‘does * know how’ withpatterns in the Candidate Pool
‘does someone know how’ ▪ Functional Compatibility- Impersonal pronouns▪ Empirical Support – 1/3
‘does somebody know how’▪ Functional Compatibility - Impersonal pronouns▪ Empirical Support – 0▪ Pattern still retained – there might be support for somebody later on in the iterative
process
‘does john know how’▪ Pattern discarded
Functional Compatibility 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)-Impersonal pronouns (e.g., someone, anybody, whichever) ‘Someone tell me where can I find X’
1Linguistic Inquiry Word Count,LIWC, http://liwc.net
Other details in the paper..
Over iterations, single-word substitutions, functional usage and empirical support conservatively expands Sis
Infusing new patterns and seed words
Stopping conditions
Sample Extracted Patterns
does anyone know how
anyone know how to
i dont know what
know where i can
tell me how to
i dont 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
i dont 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 i dont know
now i dont know
but i dont 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
Identifying the Monetization Potential of a new post
Information Seeking patterns generated offline
Monetization Potential of a post calculated by Finding its Information Seeking score : Extracting
and comparing patterns in posts with extracted patterns +
Finding its Transactional Intent Score: Using the LIWC ‘Money’ dictionary ▪ 173 words and word forms indicative of transactions,
e.g., trade, deal, buy, sell, worth, price etc.
Benchmarking with Facebook Marketplace
Using a training corpus of 8000 user posts MySpace Computers, Electronics, Gadgets forum Generated 309 unique new Information Seeking
patterns
Test Set: Using 3 sets of 150 posts each from Facebook ‘to buy’ Marketplace All these posts have Information Seeking and
Transactional intents 81 % of these posts were identified as monetizable in
nature using our algorithm
Validates usefulness of action patterns
Identifying KeywordsOff-topic Noise Elimination from posts with Monetization Potential
Identifying Keywords for Advertizing
Identifying keywords in monetizable posts Plethora of work in this space
Off-topic noise removal is our focusI 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. omgg i cant figure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleasssse, help? :(
Conceptual Overview – Details in Paper
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 Relatedness determined using concepts of information
gain Counts from Web as a corpus Makes for a domain independent solution
Off-topic Chatter - Example
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']
EvaluationsOngoing Work
What are we evaluating..
Ideally, we would like to deploy on SNSs and observe click throughs
Approximating with subgoals
1. Effectiveness of using topical keywords instead of entire post content
2. Effectiveness of using user generated content on SNSs instead of profile (homepage) information
User Study – Set Up
Keywords from 60 picked monetizable user posts 45 MySpace Forums, 15 Facebook
Marketplace split into 10 sets of 6 posts each
30 graduate students, each set of 6 posts evaluated by 3 randomly selected users
1. Effectiveness of using topical keywords
Google AdSense ads for user post content vs. extracted topical keywords
Instructions – Example
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!
Result - 2X Relevant Impressions
Users picked ads relevant to the post At least 50% inter-evaluator agreement
For the 60 posts based on content 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
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)
Qsn asked: Select ads that generate interest, captured attention
Result - 8X Generated Interest Using profile ads
Total of 56 ad impressions 7% of ads generated interest
Using user submitted posts (entire content, already monetizable) Total of 56 ad impressions 43% of ads generated interest
Using topical keywords from submitted posts Total of 59 ad impressions 59% of ads generated interest
To note…
User studies small and results preliminary, but clearly suggest Monetization potential in user activity Improvement for Ad programs in terms of
relevant impressions
Evaluations based on forum, marketplace Verbose content May not work as well for micro-blog like
content, status updates etc.
To note…
A world between relevant impressions and clickthroughs Objectionable content, vocabulary
impedance, Ad placement, network behavior Our works fits in a pipeline of other
community efforts
No profile information taken into account Cannot custom send information to Google
AdSense
Thank you
Social Media Content Analysis @ Kno.e.sis Google/Bing: Meena Nagarajan
[email protected] http://knoesis.wright.edu/students/meena/
Google/Bing: Amit Sheth [email protected] http://knoesis.org/amit
Sponsors: NSF (Semantic Discovery - SemDis), IBM UIMA Innovation Award 2007: "UIMA-based Infrastructure for Summarizing Casual,
Unstructured Text”, Microsoft's Beyond Search - Semantic Computing and Internet Economics Award 2008: Chatter, Intent and Good Karma for Targeted Advertising in Social Networks