Some Methodological Thoughts on Using Text Mining for Frame Analysis of Media Content

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Some Methodological Thoughts on Using Text Mining for Frame Analysis of Media Content

  1. 1. Computer Support for Frame Analysis of Media Content: Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK Some Methodological Thoughts Yuwei Lin ESRC National Centre for e-Social Science, University of Manchester http://www.ncess.ac.uk
  2. 2. Acknowledgement  JISC-funded 18-month TMFA Project: Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK Using Text Mining for Frame Analysis of Media Content  Key members: Sophia Ananiadou, June Finch, Peter Golding, Peter Halfpenny, Thomas Koenig, Yuwei Lin, Elisa Pieri, Rob Procter, Brian Rea, Farida Vis, Davy Weissenbacher (in alphabetical order)
  3. 3. Outline  A STS-informed paper on the impact Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK of computerisation on doing social research  Challenges of frame analysis  Text mining technologies  Some methodological issues  Concluding remarks
  4. 4. Challenges of Frame Analysis  Labour-intensive manual coding Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK  Error-prone, biased, subjective/interpretative  Non-scalable (small corpora): difficult to deal with increasingly large amount of data  Solutions: More analysts (but low inter-coder reliability) or Computerising the analysis  Trend of bridging the long-standing tension between quantitative (statistics) and qualitative (meanings) methods
  5. 5. Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK Text-mining  the opportunity of processing large amounts of textual data systematically, reducing human errors, and saving time  the potential to at least partly automate the generation of frames  add-on feature to Computer-Assisted Qualitative Data Analysis Software (CAQDAS) packages
  6. 6. Some Methodological Thoughts  Does corpus size matter? Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK  Conceptual validity and generalisability  Whose interpretations / what assumptions?  Conceptual validity and generalisability  Levels of meanings  Conceptual validity and generalisability  Standardisation of units of measurements  Clarity and transparency in doing analysis
  7. 7. Does corpus size matter?  Corpus building: small but focused, or Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK large, noisy but indiscriminative  What to include in a corpus?  How to reduce noise in raw data?  Where does human interpretation end?  Does corpus size have any impact on the conceptual validity and generalisability of frames? (quantitative or qualitative)
  8. 8. Whose interpretations and what assumptions?  Manual coding results may be subjective, Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK interpretative, biased.  In the case of computer-supported analysis:  which text mining algorithms/techniques to adopt?  based on which techniques (statistical ones?) and on which training datasets?  These techniques and corpora reflect certain interpretations, assumptions and world views.
  9. 9. Levels of meanings in frames  Diversity in doing frame analysis and Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK different definitions of frames  Various types of frames: multiple frames, overlapping frames, frames that shape people's actions and their involvement in everyday activities (Goffman)  How applicable are the lexical frames extracted by text mining techniques?
  10. 10. Standardisation of Units of Measurements Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK  Frames exist in different levels: words, sentences, paragraphs, articles (units of measurement)  Interpretative flexibility in manual coding  Debate on clarity and transparency  Text mining is systematising and standardising the units of measurement.  More objective? More reliable? More biased? More transparent or more black-boxed?
  11. 11. Concluding remarks  Labour-intensive, error-prone manual coding Yuwei Lin, 15 Jan 2009, MeCCSA 2009, Bradford, UK  CAQDAS (particularly those with text mining)  Methodological issued posed by computerisation:  Does corpus size matter?  Whose interpretations and what assumptions?  Levels of meanings in frames  Standardisation of units of measurements  Depending on research questions & contexts

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