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Discovering and Navigating Memes in Social Media

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Discovering and Navigating Memes in Social Media

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Invited talk at SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction (April 3, 2012). Based on paper by Ryu, Lease, and Woodward, to appear at ACM HyperText 2012. Joint work with Hohyon Ryu and Nicholas Woodward.

Invited talk at SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction (April 3, 2012). Based on paper by Ryu, Lease, and Woodward, to appear at ACM HyperText 2012. Joint work with Hohyon Ryu and Nicholas Woodward.

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Discovering and Navigating Memes in Social Media

  1. 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. 2. April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 2
  3. 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. 4. Inspiration #1: Living Stories livingstories.googlelabs.com April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 4
  5. 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. 6. Inspiration #2: Meme Tracker April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 6
  7. 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. 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. 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. 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. 11. Meme Browser April 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 11
  12. 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. 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

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