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Content analysis

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Goals and Types of Content Analysis
– Goals of Content Analysis: identifying analyzed data, defining data, determining population, considering context, establishing boundaries, measuring relevant aspects
– Qualitative and Quantitative Content Analysis: differences and overlaps between the two approaches
– Codebooks: purpose, construction, and importance in content analysis
– Computational Tools: advantages and limitations of using computer-based methods in content analysis
– Reliability and Validity: importance of consistency and agreement in classification, measurement of inter-coder and intra-coder reliability

Kinds of Text
– Written text, oral text, iconic text, audio-visual text, hypertexts

History and Development
– Origins of content analysis in the late 19th century
– Early applications of content analysis in studying literature and texts
– Contributions of Harold Lasswell and Bernard Berelson to content analysis
– Latent and manifest content: differences in interpretation

Uses of Content Analysis
– Inferences about communication antecedents, characteristics, and effects
– Analyzing disputed authorship, individual traits, cultural aspects, legal and evaluative evidence
– Describing trends, assessing responses, measuring readability and information flow
– Limitations of content analysis when direct measurement techniques are available

References and Key Resources
– References to books and articles on content analysis methodology and applications
– DOCA – Database of Variables for Content Analysis: examples of specific content analysis studies
– Key references on automatic content analysis, sentiment analysis, and comparison with human coding
– Foundations of Content Analysis: discussions on reliability, validity, and omissions in quantitative research

Content analysis (Wikipedia)

Content analysis is the study of documents and communication artifacts, which might be texts of various formats, pictures, audio or video. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. 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 texts or artifacts which are assigned labels (sometimes called codes) to indicate the presence of interesting, meaningful pieces of content. By systematically labeling the content of a set of texts, researchers can analyse patterns of content quantitatively using statistical methods, or use qualitative methods to analyse meanings of content within 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 pre-determined linguistic, semantic, and psychological characteristics.

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