Content Analysis of Digital Text and Its Applications
| DOI | 10.1177/2321023019838653 |
| Published date | 01 June 2019 |
| Date | 01 June 2019 |
| Subject Matter | Notes on Methods |
Notes on Methods
Content Analysis of Digital Text
and Its Applications
Anustubh Agnihotri1
Rahul Verma2
Introduction
While analysis of written text is not new as scholars in the past have spent considerable efforts in
analysing monographs, ideological pamphlets, speeches and news reports to gain political and sociologi-
cal insights, the field of content analysis is undergoing a massive methodological transformation. On one
hand, digitization of written records across the world—books, government documents, religious preach-
ing and political party materials—is producing massive amounts of machine-readable text. On the other
hand, advent of digital social media platforms has democratized political communication. Earlier, only a
select few could use the written word to engage with the mass public but now, most of us are part of
public platforms, such as Facebook and Twitter, where we express our political opinions.
This note aims to highlight the availability of new methodological tools to analyse written texts more
effectively. We begin by briefly summarizing four examples that have been used throughout this note to
illustrate technical points and help the reader grasp the full scope of the methodological innovations in
the field of automated content analysis. We then describe the logistics behind converting political texts
into formats that can be quantitatively analysed. In this section, we also discuss the methodology behind
modelling a corpus of text for creating political variables, and the different statistical techniques being
used by researchers to make sense of the political and social world. We then shift our focus to the impor-
tance of validating statistical models to ensure that the findings capture the underlying political pro-
cesses. We end the discussion by talking about advantages as well as limitations of statistical techniques
used to analyse text-based data.
Studies in Indian Politics
7(1) 83–89, 2019
© 2019 Lokniti, Centre for the
Study of Developing Societies
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DOI: 10.1177/2321023019838653
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1 Charles and Louise Travers Department of Political Science, University of California, Berkeley, CA.
2 Centre for Policy Research, New Delhi, India.
Corresponding author:
Anustubh Agnihotri, Travers Department of Political Science, University of California, 210 Barrows Hall #1950,
Berkeley, CA.
E-mail: anustubh@berkeley.edu
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