SlideShare una empresa de Scribd logo
1 de 13
Discovering and Navigating Memes
               in Social Media
                              Matt Lease
                         School of Information
                      University of Texas at Austin
                        ml@ischool.utexas.edu
                              @mattlease


                            Joint Work with
                    Hohyon Ryu & Nicholas Woodward


Paper to appear at HyperText 2012: 23rd ACM Conference on Hypertext and Social Media
April 3, 2012   SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   2
Critical Reading (Literacy)
      • Context-awareness (how work is situated)
                – Related works, Time/Place, Author…
      • Recognizing & questioning
                – Sources of Influence
                – Positions, Assumptions, Bias, …
      • New challenges online
                – Scale, authorship, citing of sources, borrowing…
      • Traditional approach: education
April 3, 2012       SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   3
Inspiration #1: Living Stories




                     livingstories.googlelabs.com
April 3, 2012    SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   4
Memes
• Similar phrases found across multiple sources
      – Includes multiple phrasings of same idea
• Re-use reveals implicit network
      – Sources, Individuals, Communities
      – Patterns of re-use reinforce links
• Questions
      – Re-use?
      – Intended re-use?
      – Visible (quoted)?
April 3, 2012   SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   5
Inspiration #2: Meme Tracker




April 3, 2012    SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   6
Where Repeated Text Occurs
      • Intended Re-use
                – Visible (Quotation): “to be or not to be”
                    • Leskovec et al., KDD’09 ( memetracker.org )
                – Hidden: e.g. plagiarism, false plurality
                – Unmarked
                    •   Near-Duplicate documents
                    •   Boilerplate: All rights reserved
                    •   Common adage: …a penny saved…
                    •   Style, genre, laziness, …
      • Accidental borrowing
      • Shared context (e.g. named entities)
                – E.g. named-entities: S. Skiena et al., Stony Brook ( textmap.com )
      • Chance (e.g. …then he said…)
April 3, 2012           SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   7
Data
      • TREC Blogs08 Collection
                – http://ir.dcs.gla.ac.uk/test_collections/blogs08info.html
                – 28M permalinks (January 2008 – January 2009)
                – 250G compressed
      • ICWSM 2009 Spinn3r Blog Dataset
                – http://www.icwsm.org/data/
                – 44 million blog posts (August - September, 2008)
                – 27 GB compressed
      • ICWSM 2011 Spinn3r Blog Dataset

April 3, 2012       SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   8
Inspiration #3: Popular Passages
      • Kolak & Schilit, HyperText’08
      • Find re-use in scanned books
                – Find repeated phrases
                – Group related phrases
                – Rank passages
                – MapReduce processing architecture
      • Browsing interface with generated links
      • Issues: data/task, locality, details, scalability
April 3, 2012       SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   9
Processing Architecture
                                                                               Blogs08 Test Collection
                                                                                  28M posts, 1.4TB
                Preprocessing (Pseudo-MapReduce)
                Decruft & Language Identification
                HTML Strip & Near-Duplicate Detection                            16M posts, 960GB



                Common Phrase Extraction
                                                                                  15K posts, 43GB
                3 MapReduce Stages

                Common Phrase Ranking
                Daily Top 200 Phrases                                            6.2M phrases, 2GB
                1 MapReduce Process

                Common Phrase Clustering
                                                                                75K phrases, 2.6MB
                1 MapReduce Process

                Meme Browser                                                        68K memes



April 3, 2012        SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   10
Meme Browser




April 3, 2012   SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   11
Efficiency: Meme Clustering



 • From WEKA ARFF format to sparse representation
       – From ~96 hours  11 hours
 • Indexed vs. un-indexed
       – From 11 hours  16 minutes (single core)
       – From 34 minutes  3 minutes (136 cores)
 • Distributed vs. single core
       – From 11 hours  34 minutes (un-indexed)
       – From 16 minutes  3 minutes (indexed)
April 3, 2012   SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction   12
Thank You!
Joint Work with                 Matt Lease
– Hohyon (Will) Ryu             ml@ischool.utexas.edu
– Nicholas Woodward             www.ischool.utexas.edu/~ml
                                  @mattlease



                                Support
                                • FCT of Portugal / UT CoLab
                                • Amazon Web Services
Meme Browser:                   • UT Austin LIFT Award
odyssey.ischool.utexas.edu/mb   • John P. Commons Fellowship

Más contenido relacionado

Similar a Discovering and Navigating Memes in Social Media

MDST 3703 F10 Seminar 11
MDST 3703 F10 Seminar 11MDST 3703 F10 Seminar 11
MDST 3703 F10 Seminar 11
Rafael Alvarado
 
CSC 8101 Non Relational Databases
CSC 8101 Non Relational DatabasesCSC 8101 Non Relational Databases
CSC 8101 Non Relational Databases
sjwoodman
 
IASSIST 2012 - DDI-RDF - Trouble with Triples
IASSIST 2012 - DDI-RDF - Trouble with TriplesIASSIST 2012 - DDI-RDF - Trouble with Triples
IASSIST 2012 - DDI-RDF - Trouble with Triples
Dr.-Ing. Thomas Hartmann
 
Text Stream Processing Tutorial @WIMS 2012
Text Stream Processing Tutorial @WIMS 2012Text Stream Processing Tutorial @WIMS 2012
Text Stream Processing Tutorial @WIMS 2012
RENDER project
 

Similar a Discovering and Navigating Memes in Social Media (20)

Discovering Memes in Social Media
Discovering Memes in Social MediaDiscovering Memes in Social Media
Discovering Memes in Social Media
 
IASSIT Kansa Presentation
IASSIT Kansa PresentationIASSIT Kansa Presentation
IASSIT Kansa Presentation
 
MDST 3703 F10 Seminar 11
MDST 3703 F10 Seminar 11MDST 3703 F10 Seminar 11
MDST 3703 F10 Seminar 11
 
CSC 8101 Non Relational Databases
CSC 8101 Non Relational DatabasesCSC 8101 Non Relational Databases
CSC 8101 Non Relational Databases
 
Hany's Doctoral Consortium
Hany's Doctoral ConsortiumHany's Doctoral Consortium
Hany's Doctoral Consortium
 
Linked Open Data in Libraries, Archives & Museums
Linked Open Data in Libraries, Archives & MuseumsLinked Open Data in Libraries, Archives & Museums
Linked Open Data in Libraries, Archives & Museums
 
An Introduction to NOSQL, Graph Databases and Neo4j
An Introduction to NOSQL, Graph Databases and Neo4jAn Introduction to NOSQL, Graph Databases and Neo4j
An Introduction to NOSQL, Graph Databases and Neo4j
 
IASSIST 2012 - DDI-RDF - Trouble with Triples
IASSIST 2012 - DDI-RDF - Trouble with TriplesIASSIST 2012 - DDI-RDF - Trouble with Triples
IASSIST 2012 - DDI-RDF - Trouble with Triples
 
Text Mining : Experience
Text Mining : ExperienceText Mining : Experience
Text Mining : Experience
 
Semantic engagement handouts
Semantic engagement handoutsSemantic engagement handouts
Semantic engagement handouts
 
New Directions in Information Organization: A Linked Data Model with BIBFRAME
New Directions in Information Organization: A Linked Data Model with BIBFRAMENew Directions in Information Organization: A Linked Data Model with BIBFRAME
New Directions in Information Organization: A Linked Data Model with BIBFRAME
 
Hany's JCDL Doctoral Consortium
Hany's JCDL Doctoral ConsortiumHany's JCDL Doctoral Consortium
Hany's JCDL Doctoral Consortium
 
A Metadata Application Profile for KOS Vocabulary Registries (KOS-AP)
A Metadata Application Profile for KOS Vocabulary Registries (KOS-AP)A Metadata Application Profile for KOS Vocabulary Registries (KOS-AP)
A Metadata Application Profile for KOS Vocabulary Registries (KOS-AP)
 
Text Stream Processing Tutorial @WIMS 2012
Text Stream Processing Tutorial @WIMS 2012Text Stream Processing Tutorial @WIMS 2012
Text Stream Processing Tutorial @WIMS 2012
 
Ir1
Ir1Ir1
Ir1
 
Linked Data Workshop Stanford University
Linked Data Workshop Stanford University Linked Data Workshop Stanford University
Linked Data Workshop Stanford University
 
Transitive credit
Transitive creditTransitive credit
Transitive credit
 
Semantic Web: introduction & overview
Semantic Web: introduction & overviewSemantic Web: introduction & overview
Semantic Web: introduction & overview
 
Ml pluss ejan2013
Ml pluss ejan2013Ml pluss ejan2013
Ml pluss ejan2013
 
ESWC 2011 BLOOMS+
ESWC 2011 BLOOMS+ ESWC 2011 BLOOMS+
ESWC 2011 BLOOMS+
 

Más de Matthew Lease

The Rise of Crowd Computing - 2016
The Rise of Crowd Computing - 2016The Rise of Crowd Computing - 2016
The Rise of Crowd Computing - 2016
Matthew Lease
 

Más de Matthew Lease (20)

Automated Models for Quantifying Centrality of Survey Responses
Automated Models for Quantifying Centrality of Survey ResponsesAutomated Models for Quantifying Centrality of Survey Responses
Automated Models for Quantifying Centrality of Survey Responses
 
Key Challenges in Moderating Social Media: Accuracy, Cost, Scalability, and S...
Key Challenges in Moderating Social Media: Accuracy, Cost, Scalability, and S...Key Challenges in Moderating Social Media: Accuracy, Cost, Scalability, and S...
Key Challenges in Moderating Social Media: Accuracy, Cost, Scalability, and S...
 
Explainable Fact Checking with Humans in-the-loop
Explainable Fact Checking with Humans in-the-loopExplainable Fact Checking with Humans in-the-loop
Explainable Fact Checking with Humans in-the-loop
 
Adventures in Crowdsourcing : Toward Safer Content Moderation & Better Suppor...
Adventures in Crowdsourcing : Toward Safer Content Moderation & Better Suppor...Adventures in Crowdsourcing : Toward Safer Content Moderation & Better Suppor...
Adventures in Crowdsourcing : Toward Safer Content Moderation & Better Suppor...
 
AI & Work, with Transparency & the Crowd
AI & Work, with Transparency & the Crowd AI & Work, with Transparency & the Crowd
AI & Work, with Transparency & the Crowd
 
Designing Human-AI Partnerships to Combat Misinfomation
Designing Human-AI Partnerships to Combat Misinfomation Designing Human-AI Partnerships to Combat Misinfomation
Designing Human-AI Partnerships to Combat Misinfomation
 
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...
 
But Who Protects the Moderators?
But Who Protects the Moderators?But Who Protects the Moderators?
But Who Protects the Moderators?
 
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...
 
Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collectio...
Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collectio...Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collectio...
Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collectio...
 
Fact Checking & Information Retrieval
Fact Checking & Information RetrievalFact Checking & Information Retrieval
Fact Checking & Information Retrieval
 
Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to E...
Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to E...Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to E...
Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to E...
 
What Can Machine Learning & Crowdsourcing Do for You? Exploring New Tools for...
What Can Machine Learning & Crowdsourcing Do for You? Exploring New Tools for...What Can Machine Learning & Crowdsourcing Do for You? Exploring New Tools for...
What Can Machine Learning & Crowdsourcing Do for You? Exploring New Tools for...
 
Deep Learning for Information Retrieval: Models, Progress, & Opportunities
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesDeep Learning for Information Retrieval: Models, Progress, & Opportunities
Deep Learning for Information Retrieval: Models, Progress, & Opportunities
 
Systematic Review is e-Discovery in Doctor’s Clothing
Systematic Review is e-Discovery in Doctor’s ClothingSystematic Review is e-Discovery in Doctor’s Clothing
Systematic Review is e-Discovery in Doctor’s Clothing
 
The Rise of Crowd Computing (July 7, 2016)
The Rise of Crowd Computing (July 7, 2016)The Rise of Crowd Computing (July 7, 2016)
The Rise of Crowd Computing (July 7, 2016)
 
The Rise of Crowd Computing - 2016
The Rise of Crowd Computing - 2016The Rise of Crowd Computing - 2016
The Rise of Crowd Computing - 2016
 
The Rise of Crowd Computing (December 2015)
The Rise of Crowd Computing (December 2015)The Rise of Crowd Computing (December 2015)
The Rise of Crowd Computing (December 2015)
 
Toward Better Crowdsourcing Science
 Toward Better Crowdsourcing Science Toward Better Crowdsourcing Science
Toward Better Crowdsourcing Science
 
Beyond Mechanical Turk: An Analysis of Paid Crowd Work Platforms
Beyond Mechanical Turk: An Analysis of Paid Crowd Work PlatformsBeyond Mechanical Turk: An Analysis of Paid Crowd Work Platforms
Beyond Mechanical Turk: An Analysis of Paid Crowd Work Platforms
 

Último

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Último (20)

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 

Discovering and Navigating Memes in Social Media

  • 1. Discovering and Navigating Memes in Social Media Matt Lease School of Information University of Texas at Austin ml@ischool.utexas.edu @mattlease Joint Work with Hohyon Ryu & Nicholas Woodward Paper to appear at HyperText 2012: 23rd ACM Conference on Hypertext and Social Media
  • 2. April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 2
  • 3. Critical Reading (Literacy) • Context-awareness (how work is situated) – Related works, Time/Place, Author… • Recognizing & questioning – Sources of Influence – Positions, Assumptions, Bias, … • New challenges online – Scale, authorship, citing of sources, borrowing… • Traditional approach: education April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 3
  • 4. Inspiration #1: Living Stories livingstories.googlelabs.com April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 4
  • 5. Memes • Similar phrases found across multiple sources – Includes multiple phrasings of same idea • Re-use reveals implicit network – Sources, Individuals, Communities – Patterns of re-use reinforce links • Questions – Re-use? – Intended re-use? – Visible (quoted)? April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 5
  • 6. Inspiration #2: Meme Tracker April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 6
  • 7. Where Repeated Text Occurs • Intended Re-use – Visible (Quotation): “to be or not to be” • Leskovec et al., KDD’09 ( memetracker.org ) – Hidden: e.g. plagiarism, false plurality – Unmarked • Near-Duplicate documents • Boilerplate: All rights reserved • Common adage: …a penny saved… • Style, genre, laziness, … • Accidental borrowing • Shared context (e.g. named entities) – E.g. named-entities: S. Skiena et al., Stony Brook ( textmap.com ) • Chance (e.g. …then he said…) April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 7
  • 8. Data • TREC Blogs08 Collection – http://ir.dcs.gla.ac.uk/test_collections/blogs08info.html – 28M permalinks (January 2008 – January 2009) – 250G compressed • ICWSM 2009 Spinn3r Blog Dataset – http://www.icwsm.org/data/ – 44 million blog posts (August - September, 2008) – 27 GB compressed • ICWSM 2011 Spinn3r Blog Dataset April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 8
  • 9. Inspiration #3: Popular Passages • Kolak & Schilit, HyperText’08 • Find re-use in scanned books – Find repeated phrases – Group related phrases – Rank passages – MapReduce processing architecture • Browsing interface with generated links • Issues: data/task, locality, details, scalability April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 9
  • 10. Processing Architecture Blogs08 Test Collection 28M posts, 1.4TB Preprocessing (Pseudo-MapReduce) Decruft & Language Identification HTML Strip & Near-Duplicate Detection 16M posts, 960GB Common Phrase Extraction 15K posts, 43GB 3 MapReduce Stages Common Phrase Ranking Daily Top 200 Phrases 6.2M phrases, 2GB 1 MapReduce Process Common Phrase Clustering 75K phrases, 2.6MB 1 MapReduce Process Meme Browser 68K memes April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 10
  • 11. Meme Browser April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 11
  • 12. Efficiency: Meme Clustering • From WEKA ARFF format to sparse representation – From ~96 hours  11 hours • Indexed vs. un-indexed – From 11 hours  16 minutes (single core) – From 34 minutes  3 minutes (136 cores) • Distributed vs. single core – From 11 hours  34 minutes (un-indexed) – From 16 minutes  3 minutes (indexed) April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 12
  • 13. Thank You! Joint Work with Matt Lease – Hohyon (Will) Ryu ml@ischool.utexas.edu – Nicholas Woodward www.ischool.utexas.edu/~ml @mattlease Support • FCT of Portugal / UT CoLab • Amazon Web Services Meme Browser: • UT Austin LIFT Award odyssey.ischool.utexas.edu/mb • John P. Commons Fellowship