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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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