Computerized content analysis of online data – opportunities for marketing scholars and practitioners
Bibliographic record
Abstract
Purpose The purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (AI)-supported ways to analyze content (primarily text) in marketing and consumer research. A further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data. Design/methodology/approach This conceptual paper maps our methods used for content analysis in marketing and consumer research. Findings This paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. The main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of AI-supported tools for all types of content and media. Originality/value This paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. It concludes that, much like in the past, today’s research focuses on the producers of the words than the words themselves and urges researchers to use AI and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".