citizen sensing, social media analytics, and applications

Citizen Sensor Data Mining, Social Media Analytics and

Development Centric Web Applications.Tutorial at

Semantic Technology Conference, San Francisco, CA.

Karthik GomadamAccenture Technology Labs,

San Jose

Amit ShethKno.e.sis @

Wright State University

Selvam VelmuruganeMoksha, Kiirti

Monday, June 6, 2011

Lu Chen(Sentiment Analysis)

Meena Nagarajan(Content Analysis)

Ashutosh Jadhav(Event Analysis)

Hemant Purohit(People & Network analysis)

Pavan Kapanipathi(Real Time Web)

Selvam Velmurugan (Kiirti, eMoksha NGOs)

Pramod Anantharam(Social & Sensor web)

Amit Sheth(Semantic Web)


Much of the work discussed in this tutorial is primarily the doctoral research by Dr. Meena Nagarajan, currently at IBM Almaden. It also includes current work done at kno.e.sis center at Wright State University.

A Quick Word


Citizen Sensing: Role, Enablers, Apps

Systematic Study Social Media

Citizen Sensing @ Real-time

Emerging Research Areas Spam and Trust in Social Media, Mobile Social ComputingResearch Application: Twitris

Tutorial part 2

Outline


Citizen Sensing

Everyday users of Web2.0 and social networks: Citizens ofan Internet- or Web-enabled social communityObservation and Information reported by citizens => Citizen SensingHuman-in-the-loop (participatory)sensing + Web 2.0 + mobile computing = emergence of

" citizen-sensor networks


Social Signals

The activity of observing, reporting, disseminating information via text, audio, video and built in device sensor (and smart devices), Creating social signals through aggregation, enhancement,

analysis, visualization, and interpretation.Immense potential to disseminate information quickly and in real-time


Enablers: Mobile Devices & Ubiquitous Connectivity

Mobile device fast emerging as our primary tool Redefines the way we engage with people, information,

etc. Global, Ubiquitous, always availableSense where you are, how you are,



Global, Ubiquitous, always availableSense where you are, how you are,



Sense where you are, how you are,


Mobile Platforms Hit Critical Mass Over 5 billion users 1+B with internet connected mobile devices (2010) Smartphones > Notebooks + Netbooks (2010E) 500K+ mobile phone applications 74% of mobile phone users (2.4B) worldwide texted (2007)



Enablers: Web 2.0 & Social Media

500M+ Facebook Users100M+ Twitter users, 85M+ tweets/dayInternet Users: 1.8 BlnContent dissemination medium Even for traditional media (@cnn, @nytimes)



100M+ Twitter users, 85M+ tweets/dayInternet Users: 1.8 BlnContent dissemination medium Even for traditional media (@cnn, @nytimes)



Internet Users: 1.8 BlnContent dissemination medium Even for traditional media (@cnn, @nytimes)



Content dissemination medium Even for traditional media (@cnn, @nytimes)



Types of UGC: Twitter(text/microblogs), Facebook(multimedia),YouTube(videos), Flicker(images), Blogs(text),Ping: (Social network for music)



Flicker(images), Blogs(text),Ping: (Social network for music)



Ping: (Social network for music)


Iran electionHaiti EarthquakeUS healthcare debate

Citizen Sensors in Action


Revolution 2.0 Political/Social Activism

If you want to liberate a government, give them the internet. - Wael Ghonim (Egyptian social activist)When Blitzer asked Tunisia, then Egypt, whats next?, Ghonim replied succinctly Ask Facebook.



When Blitzer asked Tunisia, then Egypt, whats next?, Ghonim replied succinctly Ask Facebook.


Citizen Journalism

Twitter Journalism


Social Media Inuence: Intelligence, News & Analysis

Many media companies useFacebook and Twitter asnews-delivery platform. Manyindividuals rely on them as newssource. News is increasingly social.


Business Intelligence Trend SpoTing, Forecasting, Brand

Tracking and Crisis ManagementSysomos : http://www.sysomos.com/Trendspotting : http://trendspotting.comSimplify : http://simplify360.com/Shoutlet : http://www.shoutlet.com/ Reputation (Defender): http://www.reputationdefender.com/


Development (Education, Health, eGov)

LiveMocha (http://www.livemocha.com/) OnlineLanguage learning tool with social engagement bridging the gap!!Soliya (http://www.soliya.net/) Dialogue between students fromdiverse " backgrounds

across the globe using latest multimedia technologiesProject Einstein (http://digital-democracy.org/what-we-do/programs/) A photography-based digital penpal programconnecting

youths in refugee camps to the world



Soliya (http://www.soliya.net/) Dialogue between students fromdiverse " backgrounds

across the globe using latest multimedia technologiesProject Einstein (http://digital-democracy.org/what-we-do/programs/) A photography-based digital penpal programconnecting




Project Einstein (http://digital-democracy.org/what-we-do/programs/) A photography-based digital penpal programconnecting



PatientsLikeMe (http://mashable.com/2010/07/13/social-media-health-trends/)TrialX (http://trialx.com)

Image: hMp://www.dragonsearchmarketing.com/blog/

social-media-development-through-visual-aids-tools/



Why People-Content-Network metadata?


Spatio - Temporal -Thematic+

People - Content - Network

Dimensions of Systematic Study of Social Media


"Who says what, to whom, why, to what extent and with what effect?" [Laswell] Network: Social structure emerges from the aggregate of relationships (ties)People: poster identities, the active effortof accomplishing interactionContent : studying the content of ommunication.

Social InformationProcessing


How does the (semantics or style of) content t into the observations made about the network?

Often, the three-dimensional dynamic of people, content and link structure is what shapes the social dynamic.

Studying Online Human Social Dynamics



Example: how does the topic of discussion, emotional charge of a conversation, the presence of an expert and connections between participants; together explain information propagation in a social network?


Metadata/Annotations

Metadata: an organized way to study types creation/extraction and storage use


The Anatomy of a Tweet


Explicit information from user proles User Names, Pictures, Videos, Links, Demographic

Information, Group memberships... Often is not updated Implicit information from user a+ention metadata Page views, Facebook 'Likes', Comments; TwiMer

'Follows', Retweets, Replies..

People Metadata: Variety of Self-expression Modes on Multiple

Social Media Platforms


People Metadata: Various Levels

Demographic

Network

Activity

Interests


People Metadata: Continued

User Demographic MetadataUser-idScreen/Display-name of userReal name of userLocation Profile Creation DateUser descriptionUser BioURL

Interest Level MetadataAuthor type Trustee/donor, journalist, blogger, scientist etc.

Favorite tweets Types of lists subscribed Style of Writing personality indicator No. of Followees Author type trend of Followees


Web Presence:User affiliationsKLOUT Score influence measure (www.klout.com)

Activity Level Metadata

Age of the prole

Frequency of posts

Timestamp of last status

No. of Posts

No. of Lists/groups created

No. of Lists/groups subscribed

Inuence Level Metadata (Inferring People Metadata from Network level Information)

No. of Followers normal, inuential

No. of Mentions

No. of Retweets/Forwards

No. of Replies

No. of Lists/groups following

No. of people following back

Authority & Hub Scores

People Metadata: Continued


Content Independent metadata " date, location, author etcContent Dependent metadata Direct content-based metadata Explicit/Mentioned Content metadata

named entities in content Implicit/Inferred Content Metadata

related named entities from knowledge sources Indirect content-based metadata (External metadata)

context inferred from URLs in content (images, links to articles, FourSquare checkins etc.)

Content Metadata


Content Dependent metadata Direct content-based metadata Explicit/Mentioned Content metadata

named entities in content Implicit/Inferred Content Metadata

related named entities from knowledge sources Indirect content-based metadata (External metadata)

context inferred from URLs in content (images, links to articles, FourSquare checkins etc.)

Content Metadata


Content Metadata


For Tweets Published date and time Location (where tweet was generated from) Tweet posting method (smart-phone, twitter.com,

clients for twitter) Author information

Content Independent Metadata


For Text messages Published date and time Origin location Recipient Carrier information



Content Dependent Metadata (Tweet) Direct Content-based Metadata

Direct Content-based Metadata

Indirect content-based metadata (External metadata)


Content Dependent Metadata

Direct Content-based Metadata


Network Metadata

Connections/Relationships (foundation for the network) matter!Structure Level Metadata

Community SizeCommunity growth rateLargest Strongly Connected Component sizeWeakly Connected Components & Max. sizeAverage Degree of SeparationClustering Coecient

Relationship Level Metadata

Type of RelationshipRelationship strengthUser Homophily based on certain characteristic (e.g., Location, interest etc.)Reciprocity: mutual relationshipActive Community/ Ties


Metadata: Creation, Extraction and Storage


Extracted Metadata Directly visible information from the user profile, tweet

content & community structureCreated Metadata After processing information in the user profile, content

and/or network structure

Metadata Creation & Extraction


Length: 144 characters; General topic: Egypt protestThis poor {sentiment_expression: {target:Lara Logan, polarity:negative}} woman! RT @THR CBS News'{entity:{type=News Agency}} Lara Logan{entity:{type=Person}} Released From Hospital{entity:{type=Location}} After Egypt{entity:{type=Country} Assault{type=topic}http://bit.ly/dKWTY0 {external_URL}

An Example


Rich Snippet, RDFa, open graph, semantic web based social data standards

Relationships/connections play central role Relationships as rst class object is important

Why Semantic Web is a standard for social metadata?


Semantic Web: A Very Short Primer


Representation RDF relationships as first class object OWLRepresenting Knowledge and Agreements:

nomenclature, taxonomy, folksonomy, ontology



Annotation RDFa, Xlink, model reference



Annotation RDFa, Xlink, model referenceWeb of Data Linked Open Data



Annotation RDFa, Xlink, model referenceWeb of Data Linked Open DataQuerying SPARQL; Rules: SWRL, RIF



Store metadata as data and use standard database techniques

Use filtering and clustering, summarization, statistics - implicit semantics

How to save and use metadata?


Use filtering and clustering, summarization, statistics - implicit semantics



Use explicit semantics and Semantic Web standards and technologies

semantics = meaningricher representation, support for relationships, contextsupports use of background knowledgebetter integration, powerful analysisSemantics- the implicit, the formal and the

powerfulSocial metadata on the Web



Metadata Extraction from Informal Text

Meena Nagarajan, Understanding User-Generated Content on Social Media, Ph.D. Dissertation, Wright State University, 2010


Characteristics of Text on Social Media


The Formality of Text


Recognize key entities mentioned in content Information Extraction (entity recognition, anaphora

resolution, entity classification..) Discovery of Semantic Associations between entities Topic Classification, Aboutness of content What is the content about? Intention Analysis Why did they share this content?

Content Analysis-Typical Sub-tasks


Topic Classification, Aboutness of content What is the content about? Intention Analysis Why did they share this content?



Intention Analysis Why did they share this content?




Sentiment Analysis What opinions are people conveying via the content?Author ProfilingWhat can we infer about the author from the content he posts?Context (external to content) extractionURL extraction, analyzing external content


Examining usefulness of multiple context cues for text mining algorithms Compensating for for informal, highly variable

language, lack of context Using context cues: Document corpus, syntactic,

structural cues, social medium, external domain knowledge

In this talk, highlighting sample metadata creation tasks: NER, Key Phrase Extraction, Intention, Sentiment/Opinion Mining

Research Eorts, Contributions in this space..


Named Entity Recognition I loved the hangover !Key Phrase Extraction

Part 1. NER, Key Phrase Extraction


Multiple Context Cues Utilized for NER in Blogs and MySpace


Multiple Context Cues Utilized for Keyphrase Extraction from TwiTer,

Facebook and MySpace


Techniques focus on relatively less explored content aspects on social

media platformsCombination of top-down, bottom-up analysis for informal text Statistical NLP, ML algorithms over large corpora Models and rich knowledge bases in a domain

Focus, Impact


NAMED ENTITY RECOGNITION


I loved your music Yesterday!It was THE HANGOVER of the year..lasted

forever.. So I went to the movies..badchoice picking GI

Janeworse now



Identifying and classifying tokens



NER in prior work vs. NER for Informal Text


NER focus in this work: Cultural Named Entities

Artifacts of Culture Name of a books, music albums, lms, video games,

etc.Common words in a language The Lord of the Rings, Lips, Crash, Up, Wanted,

Today, Twilight, Dark Knight

Cultural Named Entities


Varied senses, several poorly documented Merry Christmas covered by 60+ artists Star Trek:

movies, TV series, media franchise.. and cuisines !!Changing contexts with recent events The Dark Knight reference to Obama, health care

reformUnrealistic expectations Comprehensive sense definitions, enumeration of

contexts, labeled corpora for all senses .. NER Relaxing the closed-world sense assumptions

Characteristics of Cultural Entities


NER generally a sequential prediction problem NER system that achieves 90.8 F1 score on the

CoNLL-2003 NER shared task (PER, LOC, ORGN entities) [Lev Ratinov, Dan Roth]

Focus of approach: Spot and Disambiguate ParadigmStarting off with a dictionary or list of entities we want to spot

A Spot and Disambiguate Paradigm


Spot, then disambiguate in context (natural language, domain knowledge cues)Binary ClassificationIs this mention of the hangover in a sentence referring to a movie?

A Spot and Disambiguate Paradigm


Algorithmic Contributions Supervised Algorithms


Algorithmic Contributions Supervised Algorithms

Examples:I am watching Pattinson scenes in Twilight for the nth time.I spent a romantic evening watching the Twilight

by the bay..I love Lilys song


Multiple Senses in the Same Domain


Problem Defn Cultural Entity Identification : Music album, tracks Smile (Lilly Allen), Celebration (Madonna)Corpus: MySpace comments Context-poor utterances

" Happy 25th Lilly, Alfieis funny

Algorithm Preliminaries


Corpus: MySpace comments Context-poor utterances




Goal: Semantic Annotation of music named entities (w.r.t

MusicBrainz)



Using a Knowledge Resource for NER is not straight-forward..


Approach Overview

Scoped Relationship graphsUsing context cues from the

content, webpage title, url new Merry Christmas tune

Reduce potential entity spot size new albums/songs

Generate candidate entitiesSpot and Disambiguate


Sample Real-world Constraints

Career Restrictionsrelease your third album already..Recent Album restrictionsI loved your new album..Artist age restrictionshappy 25th rihanna, loved alfie btw.. etc.


Challenge 1: Several senses in the same domain Scoping relationship graphs narrows possible senses Solves the named entity identification problem

partially

Challenge 2: Non-music mentions Got your new album Smile. Loved it! Keep your SMILE on!

" " " "" " " "

Non-Music Mentions


Syntactic features POS Tags, Typed dependencies.. Example hereWord-level features Capitalization, QuotesDomain-level features

Using Language Features to eliminate incorrect mentions..


Supervised Learners


1800+ spots in MySpace user comments from artist pages

Keep your SMILE on! good spot, bad spot, inconclusive?

4-way annotator agreements

Madonna 90% agreement Rihanna 84% agreement

Lily Allen 53% agreement

Hand Labeling - Fairly Subjective


Daniel Gruhl, Meena Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, Context and Domain Knowledge Enhanced Entity SpoMing in Informal Text, The 8th International Semantic Web Conference,

2009: 260-276

Dictionary SpoTer + NLP Step


Highlights issues with using a domain knowledge for an IE task Two stage approach: chaining NL learners over results of domain model based spotters Improves accuracy up to a further 50% allows the more time-intensive NLP analytics to

run on less than the full set of input data

NER on Social Media Text using Domain Knowledge


" "

Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth: Multimodal Social Intelligence in a Real-Time Dashboard System, special issue of the VLDB Journal on "Data Management and Mining for Social Networks and Social Media", 2010 CHECK hMp://www.almaden.ibm.com/cs/

projects/iis/sound/

BBC SoundIndex (IBM Almaden): Pulse of the Online Music


http://www.almaden.ibm.com/cs/projects/iis/sound/

The Vision


Only 4% -ve sentiments, perhaps ignore the Sentiment Annotator on this data source?

Ignoring Spam can change ordering of popular artists

Trending popularity of artists Trending topics in artist pages

Several Insights


Billboards Top 50 Singles chart during the week of Sept 22-28 07 vs. MySpace popularity charts.User study indicated 2:1 and upto 7:1 (younger age

groups) preference for MySpace list.Challenging traditional polling methods!

Predictive Power of Data


Key Phrase Extraction


Key phrases extracted from prominent discussionson Twitter around the 2009 Health Care Reformdebate and 2008 Mumbai Terror Attack on one day

Key Phrase Extraction: Example


Different from Information ExtractionExtracting vs. Assigning Key Phrases " Focus: Key Phrase ExtractionPrior work focus: extracting phrases that summarize a document -- a news article, a web page, a journal article, a book..Focus: summarize multiple documents (UGC) around same event/topic of interest

Key Phrase Extraction from SM Text


Focus: Summarizing Social Perceptions via key phrase extractionPreserving/Isolating the social behind the social

data"What is said in Egypt vs. the USA should be viewed in

isolation

Key Phrase Extraction on SM Content


Accounting for redundancy, variability, off-topic content

" Met up with mom for lunch, she looks lovely as ever, good genes .. Thanks Nike, I love my new Gladiators ..smooth as a feather. I burnt all the calories of Italian joy in one run.. if you are looking for good Italian food on Main, Bucais the place to go.

Key Phrase Extraction on SM Content


Thematic components similar messages convey similar ideas Space, time metadata role of community and geography in communicationPoster attributes age, gender, socio-economic status reflect similar

perceptions

Social and Cultural Logic in SMC


Focus: n-grams, spatio-temporal metadata (social components) Syntactic Cues: In quotes, italics, bold; in document headers; phrases collocated with acronyms

Feature Space (common to several eorts)


Document and Structural Cues: Two word phrases, appearing in the beginning of a document, frequency, presence in multiple similar documents etc. Linguistic Cues: Stemmed form of a phrase, phrases that are simple and compound nouns in sentences etc.

Feature Space (common to several eorts)


President Obama in trying to regain control of the health-care debate will likely shift his pitch in September

" 1-grams: President, Obama, in, trying, to, regain, ..." 2-grams: President Obama, Obama in, in

trying, trying

Key Phrase Extraction: Overview


A descriptor is an n-gram weighted by: Thematic Importance

TFIDF, stop words, noun phrases Redundancy: statistically discriminatory in nature variability: contextually important

Spatial Importance (local vs. global popularity) Temporal Importance (always popular vs. currently trending)


Eliminating Off-topic Content [WISE2009]Frequency based heuristics will not eliminate off-topic content that is ALSO POPULAR


Yeah i know this a bit off topic but the other electronics forum is dead right now. im looking for a good camcorder, somethin not to large that can record in full HD only ones so far that ive seen are sonysCanonHV20.Great little cameras under $1000.

Approach Overview


Assume one or more seed words (from domain knowledge base) C1 -['camcorder']Extracted Key words / phrases

C2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']

Gradually expand C1 by adding phrases from C2 that are strongly associated with C1Mutual Information based algorithm [WISE2009]

Approach Overview


Are the key phrases we extracted topical and good indicators of what the content is about? If it is, it should act as an effective index/search

phrase and return relevant contentEvaluation Application: Targeted Content Delivery

Key Phrases and Aboutness Evaluations


12K posts from MySpace and Facebook Electronics forums Baseline phrases: Yahoo Term Extractor Our method phrases: Key phrase extraction,

eliminationTargeted Content from Google AdSense

Targeted Content Delivery -Evaluations


Targeted Content for all content vs. extracted key phrases


User Studies and Results


TFIDF + social contextual cues yield more useful phrases that preserve social perceptionsCorpus + seeds from a domain knowledge base eliminate off-topic phrases effectively

Impact and Contributions


Intention Mining


On social networksUse case for this talk " Targeted content = content-based " advertisements " Target = user profilesContent-based advertisements CBAs " Well-known monetization model for online content

Targeted Content Delivery via Intention Mining


Circa. 2009 Content-based Ads


Circa. 2009 -Ads on Proles


Interests do not translate to purchase intents " Interests are often outdated.. " Intents are rarely stated on a profile.. Cases that do seem to work " New store openings, sales " Highly demographic-targeted ads

What is going on here


Intents in User


Content Ads Outside Proles


Non-trivial Non-policed contentBrand image, Unfavorable sentiments People are there to networkUser attention to ads is not guaranteed Informal, casual nature of content People are sharing experiences and eventsMain message overloaded with off topic content"

Targeted Content-based Advertising



I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to

do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not

fun. Pleasssse, help? :(

Learning from Multi-topic Web Documents for Contextual Advertisement, Zhang, Y., Surendran, A. C., Platt, J. C., and

Narasimhan, M.,KDD 2008


Identifying intents behind user posts on social networks Identify Content with monetization potentialIdentifying keywords for advertising in user-generated content Considering interpersonal communication & off-topic

chatter

Preliminary Results in


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 contentMonday, June 6, 2011

Scribe Intent not same as Web Search Intent 1B.People write sentences, not keywords or phrasesPresence 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.

Identifying Monetizable Intents


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 chottopspcam User post also has an entity

From X to Action PaTerns


Set of user posts from SNSsNot annotated for presence or absence of any intent

Conceptual Overview Bootstrapping to learn IS paTerns


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

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

Bootstrapping to learn IS paTerns


! !Generate set of candidate paMerns from seed words (why,when,where,how,what)

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



! !User picks 10 seed paMerns from Sc

Sis= does anyone know how, where do I nd,

someone tell me where



! !! !

Gradually expand Sis by adding Information

Seeking paDerns from Sc



! !! !

For every pis in Sis generate set of ller paMerns



.* anyone know how does .* know how

does anyone .* how does anyone know .*



Extracting and Scoring PaTerns



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 PaMern Retained

does john know how PaMern discarded


Sc= {does anyone know how, where do I nd,

someone tell me where}

pis= `does anyone know how



pis= `does anyone know how



Functional properties / communicative functions of words

From a subset of LIWC

cognitive mechanical (e.g., if, whether, wondering, nd) I am thinking about geMing X

adverbs(e.g., how, somehow, where)

(e.g., someone, anybody, whichever)

Someone tell me where can I nd X

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

Expanding the PaTern Pool


Over iterations, single-word substitutions, functional usage and empirical support conservatively expands Sis

Infusing new paMerns and seed words

Stopping conditions

Details in [WISE2009] for..


Sample Extracted PaTerns


Information Seeking paMerns generated oine

Information seeking intent score of a post

Extract and compare paMerns in posts with extracted paMerns

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.

Identifying Monetizable Posts


Identifying keywords in monetizable posts" Plethora of work in this spaceOff-topic noise removal is our focus" I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(

Keywords for Advertizing


Identifying keywords in monetizable posts Plethora of work in this spaceOff-topic noise removal is our focus I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and

ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(

Keywords for Advertising


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

Conceptual Overview (also see slides 88,89)


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']

O-topic ChaTer


Keywords from 60 monetizable user posts

Monetizable intent, at least 3 keywords in content45 MySpace Forums, 15 Facebook Marketplace, 30 graduate students

10 sets of 6 posts each Each set evaluated by 3 randomly selected usersMonetizable intents?

All 60 posts voted as unambiguously information seeking in intent

Evaluations -User Study


Google AdSenseads for user post vs. extracted topical keywords

1. Eectiveness of using topical keywords


Instructions User Study


Users picked ads relevant to the post At least 50% inter-evaluator agreementFor the 60 posts Total of 144 ad impressions 17% of ads picked as relevantFor the topical keywords Total of 162 ad impressions 40% of ads picked as relevant

Result -2X Relevant Impressions


Users profile information Interests, hobbies, TV shows.. Non-demographic informationSubmit a postLooking to buy and why (induced noise)Ads that generate interest, captured attention

2. Prole Ads vs. Activity Ads


Using profile ads

Total of 56 ad impressions 7% of ads generated interestUsing 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

Result -8X Generated Interest


User studies small and preliminary, clearly suggest Monetization potential in user activity Improvement for Ad programs in terms of relevant

impressionsEvaluations 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 click throughs Objectionable content, vocabulary impedance, Ad

placement, network behaviorIn a pipeline of other community effortsNo profile information taken into accountCannot custom send information to Google AdSense

To note


SENTIMENT / OPINION MINING


Two main types of information we can learn from user-generated content: fact vs. opinionMuch of what we read in social media (e.g., blogs, Twitter, Facebook) is a mix of facts and opinions. For example, " Latest news: Mobile web services not working in #Bahrain and Internet is extremely slow #feb14 {fact}... looks like they "learned" from #Egypt {opinion}"

Content Analysis: Sentiment Analysis/Opinion Mining


Sentiment Analysis Motivation

Which movie should I see?

What customers complain about?

Why do people oppose

health care reform?


Example: How awful that many #Egyptian artifacts are in danger of

being destroyed. What Zahi Hawass must be thinking #jan25 (read in the

tone of what were YOU thinking

Sentiment Analysis: Tasks



Classification: overall sentiment polarity: positive/neutral/negativeExample: How awful that many #Egyptian artifacts are in danger of being destroyed.overall polarity is negative Target-specific sentiment polarity: positive/neutral/negative Example: for target "egyptian artifacts", polarity is "negative for target "Zahi Hawass", polarity is "neutral



Identification & Extraction: opinion, opinion holder, opinion target

Example: opinion="awful", opinion holder="the author", target="egyptian artifacts are in danger"

Opinion="must be thinking", opinion holder="the author", target="Zahi Hawass"


Classification: Supervised: labeled training data features, differ from traditional topic classification tasks learning strategies

Unsupervised: lexicon-based approach Bootstrapping

Sentiment Analysis: Approaches



Identification & Extraction: utilizing the relations between opinion and opinion target, proximity, syntactic dependency, co-occurrence and prepared patterns/rules


Sentiment Analysis: From Tweets to polls

Lexicon-based approach for sentiment analysis of tweets:subjective lexicon from OpinionFinder (Wilson et al., 2005)Within topic tweets, count messages containing these positive and negative words defined by the lexicon

corpus: 0.7 billion tweets, Jan 2008 Oct

2009 1.5 billion tweets, Jan 2008 May

2010



subjective lexicon from OpinionFinder (Wilson et al., 2005)Within topic tweets, count messages containing these positive and negative words defined by the lexicon



2010



Within topic tweets, count messages containing these positive and negative words defined by the lexicon



2010



B.OConnor, R.Balasubramanyan, B.R.Routledge, and N.A.Smith. From Tweets to polls: Linking text sentiment to public opinion time series. In Intl.AAAI Conference on Weblogs and

Social Media, Washington,D.C.,2010.



2010


Corpus: 2.89 million tweets referring to 24 movies released over a period of three monthsSentiment Analysis Classifier:

DynamicLMClassifier provided by LingPipe linguistic analysis packagethousands of workers from the Amazon Mechanical Turk to assignsentiments (positive, negative, neutral) for a large random sample of tweetstrain the classifier using an n-gram model

Sentiment Analysis: Predicting the Future With Social Media

S. Asur and B.Huberman. Predicting the Future With Social Media. 2010. hMp://arxiv.org/abs/1003.5699


Sentiment Analysis Classifier:DynamicLMClassifier provided by LingPipe linguistic analysis packagethousands of workers from the Amazon Mechanical Turk to assignsentiments (positive, negative, neutral) for a large random sample of tweetstrain the classifier using an n-gram model




DynamicLMClassifier provided by LingPipe linguistic analysis packagethousands of workers from the Amazon Mechanical Turk to assignsentiments (positive, negative, neutral) for a large random sample of tweetstrain the classifier using an n-gram model




thousands of workers from the Amazon Mechanical Turk to assignsentiments (positive, negative, neutral) for a large random sample of tweetstrain the classifier using an n-gram model




train the classifier using an n-gram model




Observations:The opinions may not contribute toward the given target (1,2,3,6)The subjectivity and polarity of opinion clues are domain-dependent (5,7)Single words are not enough (4,7,8)

Simple lexicon-based method doesn't work.

Sentiment Analysis: Target-specic opinion identication & Classication of Tweets-Unsupervised Approach


General subjective lexicon Commonly used subjective lexicon + popular slangs learned from

Urban Dictionary

Domain-dependent sentiment lexicon Learned from domain-specic corpus

bootstrapping More than words (word/phrase/paMern)

n-gram + statistical model



Domain-dependent sentiment lexicon Learned from domain-specic corpus

bootstrapping More than words (word/phrase/paMern)

n-gram + statistical model



Sentiment Analysis: Target-specic opinion identication &

Classication of Tweets-Unsupervised Approach


Sentiment Analysis: Target-specic opinion identication &

Classication of Tweets-Unsupervised Approach

Target-specic opinion identication/extraction Shallow syntactic analysis Rules + Proximity


URL Extraction is for Tweets

FourSquare in Facebook, TwiMer

What is it in other mediums/SMS?

Content Analysis: Context Extraction, Utilization


ResolutionSemantic Context Relevance

Content Analysis: URL extraction


Personality Signals Blogs, Style of WritingPsychometric analysis of contentSample study: Gendered writing styles online

Author Categorization: Using Content to derive additional

People metadata


Interesting questions to ask: Who are the most popular people* in the network Who are the most influential people in the network Who are the most active people in the network What are the types of people in communities of the

network Who are the bridges between communities in the network

People Analysis: Using Network to derive People metadata


By Link Analysis AlgorithmsHits [K-99]& variants PageRank [BP-97]& variants etc..Links not sufficient! Million Follower Fallacy[C-10]

People Analysis: Inuence

Source : informing-arts



Flavor of Context Analysis (activity level)Popularity NOT = Influence! Influence & Passivity[RGAH-10]Interest Similarity TwitterRank: Reciprocity & Homophily [WLJH-10]Klout Score - True Reach, Amplification [Klout]


Blogger, Scientist, Journalist,Artist, Trustee, Company X in DomainY.. Multiple types and affiliations!User interest mining Key Phrase Extraction followed by semantic association on

user bio, tweets, lists, favorite posts Twitter Study [BCDMJNRM-09]

People Analysis: User types & Aliation

Source: kahunainstitute.com


Semantic analysis of profile description Web Presence:Use of Web & Knowledge bases

(Wikipedia, Blogs)to build contextfor user types Entity Spotting & Extraction, followed by Semantic

Association and Similarity with user-type context



People Analysis: Social Engagement

Frequency Distribution Analysis of user activity posting, retweet, reply, mentions, lists etc.

Source: http://www.syscomminternational.com/


Network Analysis

Interesting questions to ask:

How communities form around topics- growth & evolution

What are the eects of presence of inuential participants in the communities

What are the eects of content nature (or sentiment, opinions) owing in network on the community life

What is the community structure: degree of separation and sub-communities

Foundation of network: NodesConnections/Relationships


Network Analysis: Methods

Source: http://www.kudos-dynamics.com/




Network Structure metricsCentrality, Connected Component, Avg.

Degree, Clustering Coecient, Avg. Path Length, Bridge, Cohesion, Prestige, Reciprocity

Important Literature: [AB-02, WS-98, BW-00; NW-06, WF-92, MW-10]


Community Discovery, growth, evolution Based on relationship types (e.g., signed network),

geography/location based etc. Hierarchical clustering algorithms Top-down, bottom-upModularity Maximization [NW-06]Algorithms comparison survey [B-06]

Network Analysis: Algorithms


Graph Partitioning & TraversalBest time-complexity & reachabilityFollow Greedy paths K-way multilevel Partitioning , Bron-Kerbosch, K-plex, K-core or N-cliques, DFS, BFS,

MST

Network Analysis: Algorithms

"We dream in Graph and We analyze in Matrix-

Barry Wellman, INSNA



Network Modeling Approaches Random graph model (Erdos-Renyi model) Small-world model(Small World Phenomenon) Scale-free model(led to Power-Law degree distribution) Social Network Analysis methods Centrality (Degree, Eigenvector, Betweenness, Closeness) Clusters (Cliques and extensions, Communities)



Information Flow: Diffusion Maximizing Spread (Opinion, Innovation, Recommendation) Outbreak Detection (e.g., disease)Social Network: No info about user action Understanding dynamics is challenging!Power Law distribution [LAH-07]Factors impacting flow: Sampling strategy, user Homophily, content nature

[CLSCK-10, NPS-10]

Network Analysis: Diusion & Homophily


Querying


(Network WorkBench)NWBTruthy Graph-toolOrangePajekTuliphttp://en.wikipedia.org/wiki/social_network_analysis_software

Analysis & Visualization Tools

Source: hMp://truthy.indiana.edu/


Event Detection


Citizen Sensing in Real-time


People cant wait forInformation500 years ago

Single life time20 years ago

Next day or two Television,News papers

Presently

Minutes are notconsideredfast enough Digital media,Social media

Real-Time Motivation


Is Real-Time the future of Web?Social Media for Real-Time Web Disaster Management

Ushahidi Real-Time Markets

Examples Brand Tracking

Twarql Movie reviews

Real-Time Social Media


Scenario

The GuardianFeb 2010


Scenario

Journalist

The GuardianFeb 2010


Information Overload Can we aggregate, organize and collectively analyze data

Real Time Can we deliver the data as it is generated

Challenges


Expressive description of Information need

Using SPARQL (Instead of traditional keyword search)Flexibility on the point of view

Ability to "slice and dice" the data in several dimensions: thematic, spatial, temporal, sentiment etc..

Streaming data with Background Knowledge

Enables automatic evolution and serendipityScalable Real-Time delivery

Using sparqlPuSH (SFSW'10)

A Semantic Web Approach


Concept Feed


Architecture


Social Sensor Server


Named Entity Recognition 2 Million Entities from DBPedia Load as Trie for efficiency N-grams matched Example: Obama, Barack Obama

Metadata Extractions (Social Sensor Server)


URL, HashTag Extraction Regex extraction Resolution URL Resolution: Follows http redirects for resolution HashTag Resolution:Tagdef, Tagal,WTHashTag.com



Other Metadata provided by Twitter User profile: User Name, Location, Time etc.. Tweet: RT, reply etc..



RDF Annotation Common RDF/OWL Vocabularies FOAF -(foaf-project.org) Friend of aFriend SIOC- (sioc-project.org) Semantically Interlinked

Online Communities

OPO -(online-presence.net) Online PresenceOntology MOAT -(moat-project.org) Meaning Of A Tag

Structured Data(Social Sensor Server)


A snippet of the annotation

rdf:type sioct:MicroblogPost ; sioc:content Fingers crossed for the upcoming #hcrvote

sioc:hascreator ; foaf:maker ;

moat:taggedWith dbpedia:Healthcare_reform . geonames:locatedIn

Dbpedia:Ohio .



Semantic Publisher


Virtuoso to store triplesQueries formulated by the users are storedSPARQL protocol over the HTTP to access rdf from the storeCombine data from tweet with the background knowledge in the rdf store

Semantic Publisher


Application Server & Distribution Hub


Distribution Hub PUSH Model - Pubsubhubbub protocol Pushes the tweets to the Application Server

Application Server Delivers data to the Clients RSS Enable Concept feeds

Application Server & Distribution Hub


?competitor

?category

?tweet dbpedia:IPad

moat:taggedWith

skos:subjectskos:subject

skos:subject

Background Knowledge (e.g. DBpedia)

@anonymizedLorem ipsum bla bla this is an example tweet

Brand Tracking - Example


?competitor

?category

?tweet dbpedia:IPad

moat:taggedWith


category:Wi-Fi category:Touchscreen

skos:subject





?competitor

?category

?tweet dbpedia:IPad

moat:taggedWith


category:Wi-Fi category:Touchscreen

skos:subject



HPTabletPCIPhone



1242 Articles from Nytimes

Around 800,000 tweets




President Obama lays out plan for

Health care reform in Speech to Joint

Session of Congress (10th Sept

Timeline.com)




President Obama lays out plan for

Health care reform in Speech to Joint

Session of Congress (10th Sept

Timeline.com)

Obama taking an active role in Health talks in pursuing his proposed overhaul

of health care system. (13th Aug

Nytimes)Monday, June 6, 2011

Twarql on Linked Open Data


Emerging Research Areas


Reasons for spamming include: Gaining Popularity Use of popular topic related keywords (e.g. hashtags of

trending topics) to propagate something off topic.

Launching malicious attacks Phishing attacks, virus, malware etc. Misleading the masses Propagating false information [MM-10].

Spam in Social Networks



Gaining popularity using trending keywords:This tweet uses #Cairo but refers to a fashion website.



Gaining popularity using trending keywords:This tweet uses #Cairo but refers to a fashion website. Egypt

Protests


Spam detection Content-based features ContentSize,URL type, spam words

Metadata-based features Account information, behavior.

Network-based features Provenance. (e.g. content from a reliable source)



Reputation,Policy,Evidence, and Provenance used to derive trustworthiness.Illustrative examples of online cues used for trust assessment. Wikipedia: article size, number of references, author, edit

history, age of the article, edit frequency etc. Product Reviews: number of helpful, very helpful ratings,

author expertise, sentiments in comments received for a review etc.

Trust in Social Networks


We propose trust ontology[AHTS-10] that Captures semantics of trust. Enables representation and reasoning with trust.Semantics of Trust specifies, for a given trustor and trustee, the following features. Type - Type of trust relationship. Scope - Context of the trust relationship. Value - Quantifies the trust relationship.



Gleaning primitive (edge) trust Trust value between two nodes is quantified using

numbers. E.g., [0,1] or [-1,1] or partial ordering[TAHS-09].Gleaning composite (path) trust Propagation via chaining and aggregation (transitivity)Some popular algorithms for trust computation Eigentrust, Spreading Activation, SUNNY etc.



Machine sensor observations are quantitative in nature, while human observations can be both qualitative and quantitative.Benefits of combining observations from humans and machine sensors Complementary evidence. Corroborative evidence

Integrating Social And Sensor Networks


Applications of integrating heterogeneous sensor observations Situation Awareness by using human observations to

interpret machine sensor observations. Enhancing trustworthiness using corroborative evidence.

Integrating Social And Sensor Networks


Instant Discovery: Geo-tagging and location-aware services, in combination with search, have made discovery a two-way street.

Compressed Expression: Mobile makes social networking even more compelling

Outsourced Memory: Cloud-based servers to store all of their mobile applications and databases

Mobile Social Computing


Compressed Expression: Mobile makes social networking even more compelling





Automated Decisions: Smart apps helps to make faster decisions or even apps makes decisions for usPeer Power: Mobiles can create social movements based on peer influence


Personalized Branding: advertising are rapidly becomingpersonalized based onindividual's needs and preferencesMobiles in social development becoming an integral part of development Coordination in disaster situations Health care delivery, especially in developing countries Elections and other forms of political expression

Mobile Social Computing (Cont.)


Research Application: Twitris


1. Information OverloadMultiple events around usWHAT to be aware ofMultiple Storylines aboutsame event!!

Twitris - Motivation


2. Evolution of Citizen Observation with location and time



3. Semantics of Social perceptions

What is being said about an event (theme) where (spatial) When (temporal )

Twitris lets you browse citizen reports using social perceptions as the fulcrum



Facilitates understanding of multi-dimensional social perceptions over SMS, Tweets, multimedia Web content, electronic news media

Twitris: Semantic Social Web Mash-up


Twitris: Architecture


Twitris: Functional Overview


Twitris: Event Summarization 1


Sentiment Analysis using statistical and machine learning techniques



Entity-relationship graph

using semantically annotated DBpedia entities mentioned in the tweets



http://twitris.knoesis.org/

http://knoesis1.wright.edu/sidfot/

Twitris: Demo, Quick Show


Twitris: On going work


Domain models to enhance understanding of the content

Twitris: Knowledge-Enabled Computing


Great role in military and NGOrescue operations during emergencies:Haiti and Chile Earthquakes

Twitris: Coordination


Coordinating needs and resources in disaster situation Analyze SMS and Web reports from disaster location Use domain models for efficient and timely coordination

Twitris: Coordination


Modeling relationships between social behavior,roles, social and cultural values, etc.

Twitris: Socio-Cultural-Behavior Model as Lens


We simply do not have enough genes to program the brain fully in advance, we must work together, extending and supporting our own intelligence with social prosthetic systems that make up for our missing cognitive and emotional capacities:Evolution has allowed our brains to be configured during development so that we are plug compatible with other humans, so that others can help us extend ourselves.- Harvard "Group Brain Project"

Collaboration


Open Source Linux,Apache, ...Social Networks Facebook, Twitter, ...Crowd Sourcing Wikipedia, Kiva, Ushahidi, Kiirti, SwiftRiver, Sahana...Collaborative Governance Peer-to-Patent, ...

Beginnings


http://gomadam.org/tutorial

@namelessnerd


Facebook + Twitter Iran post-election protests Tunisia,Egypt, Libya, Bahrain, ... Ushahidi Kenya Violence India, Lebanon, Afghanistan, and Sudan elections Haiti Earthquake Pakistan Floods

Popular Initiatives


Kiirti BBMP election monitoring Bangalore AutoWatch

Popular Initiatives


FixOurCity allows citizens to report, view and discuss civic issues in their locality.

FixOurCity Process Flow


Built on top of FixMyCity open-source codebaseStage I Report by Area/Ward and Street Integration with Google Map Displays Ward member name/contact details Select category of issue, description and severity Confirmation through email to avoid misuse

FixOurCity Backend


Stage II/III Normalize incoming reports to official wards and

categories Integration with Corporation website to allow auto-

forwarding and updating of reports

FixOurCity Backend


Information Collection: SMS (FrontlineSMS, Clickatell), Email, WebVisualization/Interactive Mapping: Timeline, Category, Geo-spatialAlerts: Geo-spatialAdmin: User Management, Report Moderation / Creation, Site Statistics

Ushahidi Features


Enables filtering and verification of real-time data from channels like Twitter, SMS, Email and RSS feeds.

SwiftRiver Architecture - I


Kiirti allows you to set up your own instance of the Ushahidi Platform without having to install it on your own web server. And, it provides pre-integrated Voice and SMS reporting capabilities within India.

Kiirti Features


Kiirti - Flywheel of Engagement


Sahana: a Free and Open Source Disaster Management system. A web based collaboration tool that addresses the common coordination problems during a disaster between Government groups, the civil society (NGOs) and the victims themselves.

Sahana Features


Sahana Features


Requests Management: Tracks requests for aid and matches them against donors who have pledged aid.Volunteer Management: Manage volunteers by capturing their skills, availability and allocation.

Sahana Features


Volunteer Management: Manage volunteers by capturing their skills, availability and allocation.

Sahana Features


Sahana Features


Missing Persons Registry: Report and Search for Missing Persons.Disaster Victim Identification.Shelter Registry- Tracks the location, distribution, capacity and breakdown of victims in Shelters.

Sahana Features


Hospital Management System- Hospitals can share information on resources & needs.Organization Registry- "Who is doing What & Where". Allows relief agencies to coordinate their activities.Ticketing- Master Message Log to process incoming reports & requests.Delphi Decision Maker- Supports the decision making of large groups of Experts.

Sahana Features


Organization Registry- "Who is doing What & Where". Allows relief agencies to coordinate their activities.Ticketing- Master Message Log to process incoming reports & requests.Delphi Decision Maker- Supports the decision making of large groups of Experts.

Sahana Features


Ticketing- Master Message Log to process incoming reports & requests.Delphi Decision Maker- Supports the decision making of large groups of Experts.

Sahana Features


Delphi Decision Maker- Supports the decision making of large groups of Experts.

Sahana Features


Sahana Features


Mapping- Situation Awareness & Geospatial Analysis.Messaging- Sends & Receives Alerts via Email & SMS.Document Library- A library of digital resources, such as Photos & Office documents.

Sahana Features


Peer To Patent is a historic initiative by the United States Patent and Trademark Office (USPTO) that opens the patent examination process to public participation for the first time. Peer to Patent is an online system that aims to improve the quality of issued patents by enabling the public to supply the USPTO with information relevant to assessing the claims of pending patent applications.

Peer to Patent


Twitris 2.0, a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris 2.0 addresses challenges in large scale processing of social data, preserving spatio-temporal-thematic properties.

Twitris Architecture


Online Dispute Resolution 30M+ pending cases in India's courtsPublic Policy ReviewsCrisis ManagementEffective Local Governance

Future Possibilities


http://www.nascio.org/events/2009Midyear/documents/NASCIO-KeynoteNoveck.pdfhttp://citizensensing.posterous.com/[MM-10] Eni Mustafaraj, Panagiotis Metaxas, From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search, In: Proceedings of the WebSci10: Extending the Frontiers of Society On-Line (April 2010).[AHTS-10] Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, 'Trust Model for Semantic Sensor and Social Networks: A Preliminary Report', National Aerospace & Electronics Conference (NAECON), Dayton Ohio, July 14-16th, 2010.[TAHS-09] K. Thirunarayan, Dharan K. Althuru, Cory A. Henson, and Amit P. Sheth, 'A Local Qualitative Approach to Referral and Functional Trust,' In: Proceedings of the The 4th Indian International Conference on Artificial Intelligence (IICAI-09), pp. 574-588, December 2009.

References


B.OConnor, R.Balasubramanyan, B.R.Routledge, and N.A.Smith. From Tweets to polls: Linking text sentiment to public opinion time series.In International AAAI Conference on Weblogs and Social Media, Washington,D.C.,2010.Sitaram Asur and Bernardo A.Huberman. Predicting the Future With Social Media. 2010. http://arxiv.org/abs/1003.5699A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, February 17, 2009M. Nagarajan et al., Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences, Tenth International Conference on Web Information Systems Engineering, Oct 5-7, 2009, PolandDaniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, Multimodal Social Intelligence in a Real-Time Dashboard System to appear in a special issue of the VLDB Journal on 'Data Management and Mining for Social Networks and Social Media', 2010

References


A. Sheth, C. Thomas, and P. Mehra, Continuous Semantics to Analyze Real-Time Data, IEEE Internet Computing, November-December 2010, pp. 80-85[NPS-10] M. Nagarajan, H. Purohit, and A. Sheth. A Qualitative Examination of Topical Tweet and Retweet Practices, 4th Int'l AAAI Conference on Weblogs and Social Media, ICWSM 2010[RGAH-10] D. Romero, W. Galuba, S. Asur, and B. Huberman. Influence and Passivity in Social Media. Arxiv preprint, arXiv:1008.1253, 2010[LLDM-10] J. Leskovec, K. Lang, A. Dasgupta, and M. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 6(1):29{123, 2009.[CHBG-10] M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi. Measuring user influence in twitter: The million follower fallacy. In ICWSM'04, 2010.[BP-98] S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, Vol 30, 1-7, 1998.

References


[K-99] Jon Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM 46 (5): 604 -632, 1999.[AB-02] R. Albert and A.L. Barabasi. Statistical Mechanics of Complex Networks. Rev. Modem Physics, vol. 74, no. 1, pp. 47-97, 2002.[WLJH-10] Jianshu Weng and Ee-Peng Lim and Jing Jiang and Qi He. TwitterRank: nding topic-sensitive influential twitterers. WSDM, 2010.[BCDMJNRM-09] N. Banerjee, D. Chakraborty, K. Dasgupta, S. Mittal, A. Joshi, S. Nagar, A. Rai, and S. Madan. User interests in social media sites: an exploration with micro-blogs. CIKM '09.[RCD-10] A. Ritter, C. Cherry, and B. Dolan. 2010. Unsupervised modeling of Twitter conversations. InHuman Language Technologies: ACL (HLT '10).[WS-10] D.J. Watts; S.H. Strogatz. Collective dynamics of 'small-world' networks. Nature 393 (6684): 40910, 1998

References


[NW-06] M. E. J. Newman, D. J. Watts The structure and dynamics of network, Princeton University Press, 2006[WF-92] Wasserman & Faust, Social Network Analysis, 1992[EK-10] D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010[MW-10] A. Marin and B. Wellman. Handbook of Social Network Analysis, 2010[B-06] H. Balakrishnan. Algorithms for Discovering Communities in Complex Networks. Ph.D. Dissertation. University of Central Florida, Orlando, FL, USA. Advisor(s) Narsingh Deo. 2006[CLSCK-10] M. D. Choudhury, , Y-R. Lin, H. Sundaram, K. S. Candan, L. Xie, A. Kelliher. How Does the Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?. ICWSM 2010[LAH-07] J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web 1, 1, Article 5, May 2007.

References


citizen sensing, social media analytics, and applications

Education

million follower

igot food

master message

amazon mechanical

happy 25th

alfieis funnyalgorithm

85m tweetsdayinternet

ghonim replied