Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Digital Metaforanalyse× | Digital Indholdsanalyse× | |
|---|---|---|
| Fagområde | Kvalitativ | Kvalitativ |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2000s–2010s (digital application) | 1950s (classical); digital adaptation 2000s–2010s |
| Ophavsperson≠ | Rooted in Lakoff & Johnson (1980); extended to digital contexts by corpus and computational linguists from the 2000s onward | Building on Berelson (1952) and Krippendorff (1980); adapted for digital contexts by Herring (2010) and Neuendorf (2002+) |
| Type≠ | Qualitative–interpretive analysis | Qualitative/quantitative hybrid research approach |
| Oprindelig kilde≠ | Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press. ISBN: 978-0226468013 | Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage. ISBN: 978-1412979474 |
| Aliasser | online metaphor analysis, digital metaphor research, metaphor analysis of digital texts, DMA | DCA, online content analysis, web content analysis, digital media content analysis |
| Relaterede≠ | 5 | 4 |
| Resumé≠ | Digital Metaphor Analysis (DMA) is a qualitative research approach that identifies, maps, and interprets conceptual metaphors embedded in digital texts — social media posts, online forums, blogs, comment sections, and other internet-mediated communication. Drawing on Conceptual Metaphor Theory (Lakoff and Johnson 1980), it examines how users frame abstract ideas (identity, politics, health, crisis) through systematic metaphorical mappings, revealing shared conceptual structures and ideological orientations within online discourse communities. | Digital Content Analysis is a systematic research method for describing, categorising, and interpreting the content of digital materials — social media posts, websites, online forums, blogs, emails, and video transcripts. It applies the rigorous coding logic of classical content analysis to digitally native or digitally collected text, enabling researchers to move from raw online data to structured, interpretable findings about communication, meaning, and social phenomena. |
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