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How We Do Things With Words: Analyzing Text as Social and Cultural Data.

Dong Nguyen1,2,3, Maria Liakata1,4,5, Simon DeDeo6,7

  • 1Alan Turing Institute, London, United Kingdom.

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Summary
This summary is machine-generated.

This study explores computational text analysis for social and cultural concepts, offering guidance and promoting interdisciplinary collaboration for richer insights.

Keywords:
computational social sciencecomputational text analysiscultural analyticsdigital humanitiesnatural language processing

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Area of Science:

  • Computational Social Science
  • Digital Humanities
  • Text Analysis Methodologies

Background:

  • Computational text analysis is increasingly used for social and cultural concepts.
  • Discussions often overlook complex challenges inherent in these analyses.
  • Interdisciplinary approaches are crucial but not always fully realized.

Purpose of the Study:

  • To highlight critical issues in computational text analysis of social and cultural data.
  • To provide guiding questions for researchers in this domain.
  • To foster greater interdisciplinary collaboration.

Main Methods:

  • The study draws on the authors' diverse experiences in computational text analysis.
  • It synthesizes insights from varied disciplinary backgrounds and research practices.
  • Key questions are formulated based on practical challenges and commonalities observed.

Main Results:

  • Identification of complex, often-understated issues in computational text analysis.
  • Development of a framework of guiding questions for future research.
  • Emphasis on the necessity of bridging disciplinary divides.

Conclusions:

  • Computational text analysis of social and cultural concepts requires careful consideration of complex issues.
  • A structured approach with guiding questions can improve research quality.
  • Interdisciplinary collaboration is essential for advancing the field and unlocking its full potential.