Сравнение на методи
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| Анализ на дигитални метафори× | Дигитален контент анализ× | |
|---|---|---|
| Област | Качествени методи | Качествени методи |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2000s–2010s (digital application) | 1950s (classical); digital adaptation 2000s–2010s |
| Създател≠ | 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+) |
| Тип≠ | Qualitative–interpretive analysis | Qualitative/quantitative hybrid research approach |
| Основополагащ източник≠ | 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 |
| Други названия | online metaphor analysis, digital metaphor research, metaphor analysis of digital texts, DMA | DCA, online content analysis, web content analysis, digital media content analysis |
| Свързани≠ | 5 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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