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DIGGING INTO DATA

A presentation to the NSF. DIGGING INTO DATA. Cascades, Islands, or Streams? Time, Topic and Scholarly Activities in Humanities and Social Science Research. Who is. DIGGING INTO DATA. THELWALL. HOLMBERG. LARIVIÈRE. SUGIMOTO. DING. MILOJEVIĆ. What. DIGGING INTO DATA. What.

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DIGGING INTO DATA

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  1. A presentation to the NSF DIGGING INTO DATA Cascades, Islands, or Streams? Time, Topic and Scholarly Activities in Humanities and Social Science Research

  2. Who is DIGGING INTO DATA THELWALL HOLMBERG LARIVIÈRE SUGIMOTO DING MILOJEVIĆ

  3. What DIGGING INTO DATA

  4. What DIGGING INTO DATA Time: 1743-2011 Dissertations: 2,307,555 Subjects: 166 Schools: 1,490 Countries: 66

  5. What DIGGING INTO DATA Time: 1900-2011 Medicine Articles: 14,698,810 Medicine References: 380,058,817 Social Science Articles: 4,228,702 Social Science References: 77,908,552 Arts & Humanities Articles: 3,151,986 Arts & Humanities References: 26,180,296 Natural Science Articles: 14,853,029 Natural Science References: 335,144,498

  6. What DIGGING INTO DATA Time: 2007-2012 Articles: 744,584 Broad Subject areas: 7 Matching ISI records: ~50%

  7. What DIGGING INTO DATA Time: 2010-current Tweets: 100,000 per month Subjects: 11 Generalist journals: 4 Scientists and science journalists: 350

  8. What DIGGING INTO DATA Time: 2006-2012 Videos: 1,202 Views on TED: 620,406,446 Views on YouTube: 111,681,275 Comments on YouTube: 414,311

  9. Why are we DIGGING INTO DATA Integrate several datasets representing a broad range of scholarly activities Use methodological and data triangulation to explore the lifecycle of topics within and across a range of scholarly activities Develop transparent tools and techniques to enable future predictive analyses

  10. Show me the DIGGING INTO DATA

  11. Show me the DIGGING INTO DATA

  12. Show me the DIGGING INTO DATA

  13. Show me the DIGGING INTO DATA H=Hedges: lowered certainty (“perhaps”) B=Boosters: heightened certainty (“absolutely”) SM=Self-mentions: self-references (“the author”) AM=Attitude markers: author-text positions (“admitedly”) EM=Engagement markers: reader positions (“should”)

  14. Show me the DIGGING INTO DATA

  15. Show me the DIGGING INTO DATA

  16. Keep on DIGGING INTO DATA

  17. Keep on DIGGING INTO DATA

  18. Comments DIGGING INTO DATA

  19. Analyzing sentiment DIGGING INTO DATA • We are developing sentiment analysis software SentiStrength for the texts in the project • The program will classify the sentiment of texts based upon lexicons of words – e.g., good, bad – plus special rules for negation, booster words (e.g., very) etc. • The lexicon will be customised for different genres – e.g., flawed, incomplete for academic texts, dull, inspiring for videos

  20. Lead-lag analysis DIGGING INTO DATA

  21. After DIGGING INTO DATA Scott Weingart

  22. Towards a new model DIGGING INTO DATA Formal vs. Informal Published vs. Unpublished Genres Book Review Curated DB Multimedia Article Slideshow Blog Conf. paper Report Dissemination Email Production Producer Draft Tweet Prosumer Consumer

  23. Questions about DIGGING INTO DATA Cassidy R. Sugimoto (PI) Assistant Professor School of Library and Information Science Indiana University Bloomington sugimoto@indiana.edu

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