Editing Content analysis (section)

{{Short description|Research method for studying documents and communication artifacts}}
{{Sociology}}

'''Content analysis''' is the study of [[document]]s and communication artifacts, known as texts e.g. photos, speeches or essays. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner.<ref>{{Cite book|title=Business research methods|first=Alan|last=Bryman|date=2011|publisher=Oxford University Press|last2=Bell|first2=Emma|isbn=9780199583409|edition=3rd|location=Cambridge|oclc=746155102}}</ref> One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social experiences or collecting survey answers.

Practices and philosophies of content analysis vary between academic disciplines. They all involve systematic reading or observation of [[text (literary theory)|texts]] or artifacts which are [[Coding (social sciences)|assigned labels (sometimes called codes)]] to indicate the presence of interesting, [[semantics|meaningful]] pieces of content.<ref>{{cite book|last=Hodder|first=I.|title=The interpretation of documents and material culture|year=1994|publisher=Sage|location=Thousand Oaks etc.|isbn=978-0761926870|page=155|url=https://books.google.com/books?id=CdCGek5KJ_QC&q=hodder+mute+evidence&pg=PA155}}</ref><ref name="Tipaldo 2014 42">{{cite book|url=https://www.mulino.it/isbn/9788815248329|title=L'analisi del contenuto e i mass media|last=Tipaldo|first=G.|publisher=Il Mulino|year=2014|isbn=978-88-15-24832-9|location=Bologna, IT|pages=42}}</ref> By systematically labeling the content of a set of [[text (literary theory)|texts]], researchers can analyse patterns of content [[Quantitative research|quantitatively]] using [[Statistics|statistical methods]], or use [[Qualitative research|qualitative]] methods to analyse meanings of content within [[text (literary theory)|texts]].

Computers are increasingly used in content analysis to automate the labeling (or coding) of documents. Simple computational techniques can provide descriptive data such as word frequencies and document lengths. [[Machine learning]] classifiers can greatly increase the number of texts that can be labeled, but the scientific utility of doing so is a matter of debate. Further, numerous computer-aided text analysis (CATA) computer programs are available that analyze text for predetermined linguistic, semantic, and psychological characteristics.<ref name="Neuendorf2016" />