Monetizing User Activity on Social Networks - Challenges and Experiences 

Meena Nagarajan, Amit P. Sheth KNO.E.SIS Center

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 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

Today - Content-based Ads

May 30,June 02 2009

Today - Ads on Profiles

June 01, 2009

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Interests do not translate to purchase intents Interests are often outdated..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

Intents in User Activity Elsewhere

June 01, 2009

Content Ads Outside Profiles

June 01, 2009

CONTENT-BASED ADS

ON THEIR PROFILES

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. 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

What How Why People Write

Cultural Entities

Word Usages in self-presentation

Slang sentiments

Intentions

WHAT

WHY

HOW

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

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

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 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

Conceptual Overview Bootstrapping to learn IS patterns

Set of user posts from SNSs

Not annotated for presence or absence of any intent

Bootstrapping to learn IS patterns

Generate a universal set of n-gram patterns; freq > f

S = set of all 4-grams; freq > 3

Generate set of candidate patterns from seed words

(why,when,where,how,what)

Sc = all 4-grams in S that extract seed words

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’….

Bootstrapping to learn IS patterns

Bootstrapping to learn IS patterns

Gradually expand Sis by adding Information Seeking patterns

from Sc

Sc = all 4-grams in S that extract seed

words

Sis = ‘does anyone know how’, ‘where do i find’, ‘someone tell

me where’….

Bootstrapping to learn IS patterns

For every pis in Sis generate set of filler patterns

Sis = ‘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 .*’

Look for patterns in Sc

-Functional compatibility of filler- words used in similar semantic contexts

- Empirical support for filler

‘does anyone know how’

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) impersonal pronouns (e.g., someone,

anybody, whichever)▪ ‘Someone tell me where can I find X’

1Linguistic Inquiry Word Count,LIWC, http://liwc.net

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

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

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 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.

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 %

Identifying KeywordsOff-topic noise elimination

Identifying keywords in monetizable posts Plethora of work in this space

Off-topic noise removal is our focus

Keywords for Advertizing

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? :(

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

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']

EvaluationsPreliminary work

Evaluations - User Study

Keywords from 60 monetizable user 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

1. Effectiveness of using topical keywords

Google AdSense ads for user post vs. extracted topical keywords

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!

Instructions – User Study

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

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

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

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

To note…

A world between relevant impressions 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

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

Thank you

Google/Bing: Meena Nagarajan meena@knoesis.org http://knoesis.wright.edu/students/meen

a/

Google/Bing: Amit Sheth amit@knoesis.org http://knoesis.wright.edu/amit