Content analysis

Content analysis is "a wide and heterogeneous set of manual or computer-assisted techniques for contextualized interpretations of documents produced by communication processes in the strict sense of that phrase (any kind of text, written, iconic, multimedia, etc.) or signification processes (traces and artifacts), having as ultimate goal the production of valid and trustworthy inferences."

Content analysis has come to be a sort of 'umbrella term' referring to an almost boundless set of quite diverse research approaches and techniques. Broadly, it can refer to methods for studying and/or retrieving meaningful information from documents.[1] In a more focused way, content analysis refers to a family of techniques for studying the "mute evidence" of texts and artifacts.[2] There are 5 types of texts in content analysis:

  1. written text, such as books and papers
  2. oral text, such as speech and theatrical performance
  3. iconic text, such as drawings, paintings, and icons
  4. audio-visual text, such as TV programs, movies, and videos
  5. hypertexts, which are texts found on the Internet

Content analysis can also be described as studying traces, which are documents from past times, and artifacts, which are non-linguistic documents. Texts are understood to be produced by communication processes in a broad sense of that phrase - often gaining mean through abduction.[1][3]

Despite the wide variety of options, generally speaking every "content analysis" method implies a series of transformation procedures, equipped with a different degree of formalization depending on the type of technique used, but which share the scientific re-elaboration of the object examined. This means, in short, guaranteeing the repeatability of the method, i.e.: that preset itinerary which, following pre-established techniques produced those results. This path changes consistently depending on the direction imprinted by the interpretative key of the researcher who, at the end of the day, is responsible for the operational decisions made.[4]

Over the years, content analysis has been applied to a variety of scopes. Hermeneutics and Philology have been using content analysis since the dawn of time to interpret sacred and profane texts and, in not a few cases, to attribute texts' authorship and authenticity.[1][5]

In recent times, particularly with the advent of mass communication, content analysis has known an increasing use to deeply analyse and understand media content and media logic. The political scientist Harold Lasswell formulated the core questions of content analysis in its early-mid 20th-century mainstream version: "Who says what, to whom, why, to what extent and with what effect?".[6] The strong emphasis for a quantitative approach started up by Lasswell was finally carried out by another "father" of content analysis, Bernard Berelson, who proposed a definition of content analysis which, from this point of view, is emblematic: "a research technique for the objective, systematic and quantitative description of the manifest content of communication."[7]

Quantitative content analysis has enjoyed a renewed popularity in recent years thanks to technological advances and fruitful application in of mass communication and personal communication research. Content analysis of textual big data produced by new media, particularly social media and mobile devices has become popular. These approaches take a simplified view of language that ignores the complexity of semiosis, the process by which meaning is formed out of language. Quantitative content analysts have been criticized for appealing to statistical measures to justify the objectivity and systematic nature of their methods while ignoring the limitations of their approach .


The method of content analysis enables the researcher to include large amounts of textual information and systematically identify its properties, such as the frequencies of most used keywords by locating the more important structures of its communication content. Such amounts of textual information must be categorised to provide a meaningful reading of content under scrutiny. For example, David Robertson created a coding frame for a comparison of modes of party competition between British and American parties.[8] It was developed further in 1979 by the Manifesto Research Group aiming at a comparative content-analytic approach on the policy positions of political parties. This group created the Manifesto Project Database.

Since the 1980s, content analysis has become an increasingly important tool in the measurement of success in public relations (notably media relations) programs and the assessment of media profiles, such as political media slant - orientation towards one of the two major parties.[9][10] In 1982, John Naisbitt published his popular Megatrends, based on content analysis in the US media. In analyses of this type, data from content analysis is usually combined with media data (circulation, readership, number of viewers and listeners, frequency of publication). It has also been used by futurists to identify trends.

The creation of coding frames is intrinsically related to a creative approach to variables that exert an influence over textual content. In political analysis, these variables could be political scandals, the impact of public opinion polls, sudden events in external politics, inflation etc. Mimetic Convergence, created by Fátima Carvalho for the comparative analysis of electoral proclamations on free-to-air television, is an example of creative articulation of variables in content analysis.[11] The methodology describes the construction of party identities during long-term party competitions on TV, from a dynamic perspective, governed by the logic of the contingent. This method aims to capture the contingent logic observed in electoral campaigns by focusing on the repetition and innovation of themes sustained in party broadcasts. According to such post-structuralist perspective from which electoral competition is analysed, the party identities, 'the real' cannot speak without mediations because there is not a natural centre fixing the meaning of a party structure, it rather depends on ad-hoc articulations. There is no empirical reality outside articulations of meaning. Reality is an outcome of power struggles that unify ideas of social structure as a result of contingent interventions. In Brazil, these contingent interventions have proven to be mimetic and convergent rather than divergent and polarised, being integral to the repetition of dichotomised world-views.

Mimetic Convergence thus aims to show the process of fixation of meaning through discursive articulations that repeat, alter and subvert political issues that come into play. For this reason, parties are not taken as the pure expression of conflicts for the representation of interests (of different classes, religions, ethnic groups[12][13]) but attempts to recompose and re-articulate ideas of an absent totality around signifiers gaining positivity.

Every content analysis should depart from a hypothesis. The hypothesis of Mimetic Convergence supports the Downsian interpretation that in general, rational voters converge in the direction of uniform positions in most thematic dimensions. The hypothesis guiding the analysis of Mimetic Convergence between political parties' broadcasts is: 'public opinion polls on vote intention, published throughout campaigns on TV will contribute to successive revisions of candidates' discourses. Candidates re-orient their arguments and thematic selections in part by the signals sent by voters. One must also consider the interference of other kinds of input on electoral propaganda such as internal and external political crises and the arbitrary interference of private interests on the dispute. Moments of internal crisis in disputes between candidates might result from the exhaustion of a certain strategy. The moments of exhaustion might consequently precipitate an inversion in the thematic flux.

As an evaluation approach, content analysis is considered by some to be quasi-evaluation because content analysis judgements need not be based on value statements if the research objective is aimed at presenting subjective experiences. Thus, they can be based on knowledge of everyday lived experiences. Such content analyses are not evaluations. On the other hand, when content analysis judgements are based on values, such studies are evaluations.[14]

Qualitative content analysis is “a systematic, replicable technique for compressing many words of text into fewer content categories based on explicit rules of coding”.[15] It often involves building and applying a “concept dictionary” or fixed vocabulary of terms on the basis of which words are extracted from the textual data for concording or statistical computation.

Uses of content analysis

Holsti groups fifteen uses of content analysis into three basic categories:[16]

He also places these uses into the context of the basic communication paradigm.

The following table shows fifteen uses of content analysis in terms of their general purpose, element of the communication paradigm to which they apply, and the general question they are intended to answer.

Uses of Content Analysis by Purpose, Communication Element, and Question
Purpose Element Question Use
Make inferences about the antecedents of communications Source Who?
Encoding process Why?
  • Secure political & military intelligence
  • Analyse traits of individuals
  • Infer cultural aspects & change
  • Provide legal & evaluative evidence
Describe & make inferences about the characteristics of communications Channel How?
  • Analyse techniques of persuasion
  • Analyse style
Message What?
  • Describe trends in communication content
  • Relate known characteristics of sources to messages they produce
  • Compare communication content to standards
Recipient To whom?
  • Relate known characteristics of audiences to messages produced for them
  • Describe patterns of communication
Make inferences about the consequences of communications Decoding process With what effect?
Note. Purpose, communication element, & question from Holsti.[16] Uses primarily from Berelson[17] as adapted by Holsti.[16]

The process of a content analysis

According to Dr. Klaus Krippendorff, six questions must be addressed in every content analysis:[5]

  1. Which data are analysed?
  2. How are they defined?
  3. What is the population from which they are drawn?
  4. What is the context relative to which the data are analysed?
  5. What are the boundaries of the analysis?
  6. What is the target of the inferences?

The assumption is that words and phrases mentioned most often are those reflecting important concerns in every communication. Therefore, quantitative content analysis starts with word frequencies, space measurements (column centimeters/inches in the case of newspapers), time counts (for radio and television time) and keyword frequencies. However, content analysis extends far beyond plain word counts, e.g. with Keyword In Context routines words can be analysed in their specific context to be disambiguated. Synonyms and homonyms can be isolated in accordance to linguistic properties of a language.

Qualitatively, content analysis can involve any kind of analysis where communication content (speech, written text, interviews, images ...) is categorised and classified. In its beginnings, using the first newspapers at the end of 19th century, analysis was done manually by measuring the number of lines and amount of space given a subject. With the rise of common computing facilities like PCs, computer-based methods of analysis are growing in popularity. Answers to open ended questions, newspaper articles, political party manifestoes, medical records or systematic observations in experiments can all be subject to systematic analysis of textual data. By having contents of communication available in form of machine readable texts, the input is analysed for frequencies and coded into categories for building up inferences. Robert Weber notes: "To make valid inferences from the text, it is important that the classification procedure be reliable in the sense of being consistent: Different people should code the same text in the same way".[18] The validity, inter-coder reliability and intra-coder reliability are subject to intense methodological research efforts over long years.[5]

Normally, content analysis can only be applied on manifest content; that is, the words, sentences, or texts themselves, rather than their meanings. Yet, through mixed methodologies often common in content analysis, a research is able to analysis data, as well as its meaning.

A further step in analysis is the distinction between dictionary-based (quantitative) approaches and qualitative approaches. Dictionary-based approaches set up a list of categories derived from the frequency list of words and control the distribution of words and their respective categories over the texts. While methods in quantitative content analysis in this way transform observations of found categories into quantitative statistical data, the qualitative content analysis focuses more on the intentionality and its implications.

Dermot McKeone highlighted the difference between prescriptive analysis and open analysis.[19] In prescriptive analysis, the context is a closely defined set of communication parameters (e.g. specific messages, subject matter); open analysis identifies the dominant messages and subject matter within the text.

As the uncritical use of text is today widely recognized as naive in the Social Sciences domain, we can move from the original classification by Krippendorff [5]

Reliability in content analysis

Neuendorf suggests that when human coders are used in content analysis two coders should be used. Reliability of human coding is often measured using a statistical measure of intercoder reliability or "the amount of agreement or correspondence among two or more coders".[20]

See also


  1. 1 2 3 Tipaldo, G. (2014). L'analisi del contenuto e i mass media. Bologna, IT: Il Mulino. p. 42. ISBN 978-88-15-24832-9.
  2. Hodder, I. (1994). The interpretation of documents and material culture. Thousand Oaks etc.: Sage. p. 155. ISBN 0761926879.
  3. Timmermans, Stefan and Iddo Tavory (2012). "Theory Construction in Qualitative Research: From Grounded Theory to Abductive Analysis". Sociological Theory (30(3) ed.): 167–186.
  4. Tipaldo, G. (2013). Handbook of TV quality Assessment. Preston, UK: UCLan University Publishing. p. 18. ISBN 978-0-9926349-1-9.
  5. 1 2 3 4 Krippendorff, Klaus (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Thousand Oaks, CA: Sage. p. 413. ISBN 9780761915454.
  6. Lasswell, Harold Dwight (1948). Power and Personality. New York, NY.
  7. Berelson, B. (1952). Content Analysis in Communication Research. Glencoe: Free Press. p. 18.
  8. Robertson, David Bruce (1976). A theory of party competition. London and New York: J. Wiley. ISBN 0471727377.
  9. Gentzkow, Matthew and Jesse M. Shapiro (2007). "What Drives Media Slant? Evidence from U.S. Daily Newspapers". Econometrica (78(1)): 35–71.
  10. "Methods for Media Analysis". ReStore. Economic and Social Research Council. Retrieved 13 June 2013.
  11. Carvalho, Fátima Lampreia (2000). "Continuidade e Inovação: conservadorismo e política da comunicação no Brasil" [Continuity and Innovation: Conservatism and Politics of Communication in Brazil]. Journal Revista Brasileira de Ciencias Sociais. São Paulo. 15 (43): 147–162. doi:10.1590/S0102-69092000000200008. Retrieved 12 June 2013.
  12. Lipset, Seymour M.; Stein Rokkan (1967). Cleavage structures, party systems, and voter alignments: an introduction. Free Press. pp. 1–64.
  13. Lijphart, Arend (1984). Democracies: Patterns of majoritarian and consensus government in twenty-one countries. New Haven: Yale University Press. p. 229. ISBN 0300031157.
  14. Frisbie, Richard (7–11 April 1986). The use of microcomputer programs to improve the reliability and validity of content analysis in evaluation. Annual Meeting of the American Educational Research Association. San Francisco, CA.
  15. Stemler, Steve (2001). "An Overview of Content Analysis". Practical Assessment, Research & Evaluation. 7 (17). Retrieved 12 June 2013.
  16. 1 2 3 Holsti, Ole R. (1969). Content Analysis for the Social Sciences and Humanities. Reading, MA: Addison-Wesley.
  17. Berelson, Bernard (1952). Content Analysis in Communication Research. Glencoe, Ill: Free Press.
  18. Weber, Robert Philip (1990). Basic Content Analysis (2nd ed.). Newbury Park, CA: Sage. p. 12. ISBN 9780803938632.
  19. McKeone, Dermot H. (1995). Measuring Your Media Profile: A general introduction to media analysis and PR evaluation for the communications industry. Hampshire, England: Gower Press Ltd. ISBN 9780566075780.
  20. Neuendorf, Kimberly A. (2002). The Content Analysis Guidebook. Thousand Oaks, CA: Sage. p. 10.

Further reading

External links

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