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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. ...

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 Discovering and Navigating Memes in Social Media Presentation Transcript

  • 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 WoodwardPaper 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: educationApril 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 3
  • Inspiration #1: Living Stories livingstories.googlelabs.comApril 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 TrackerApril 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 DatasetApril 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, scalabilityApril 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 memesApril 3, 2012 SBP 2012: Intl. Conf. on Social Computing, Behavioral-Cultural Modeling, & Prediction 10
  • Meme BrowserApril 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 ServicesMeme Browser: • UT Austin LIFT Awardodyssey.ischool.utexas.edu/mb • John P. Commons Fellowship