What the study analysed
The open-access article examines how identity is created and fragmented within the social media landscape, and how the resulting “fragmented Self” figures in propaganda and public opinion formation. It takes a social constructionist stance and draws on soft power, public diplomacy, social identity, network, and agenda-setting theory.
Methodologically it combines qualitative content analysis with grounded theory elements, applied in sequence, an approach the authors argue yields deeper theoretical insight than either method alone. The sample of literature sources included 51 items, drawn together by five authors from Taras Shevchenko National University of Kyiv, the State University of Trade and Economics, and three other Ukrainian institutions.
AI for category formulation, humans for theory building
Evidano was used to formulate the categories that were then carried into the constructed theory and modelling. The authors single out automatic theme extraction from qualitative data as the capability that let them move quickly from sources to candidate categories and concepts.
The inductive theory-development process itself was carried out manually, as grounded theory requires. The result is a conceptual model of digital soft power influence on fragmented identity across three dimensions: social, national, and civic identity. The split between AI-assisted category formulation and manual theory construction is stated plainly in the methodology section.
How Evidano fit the workflow
Category formulation accelerated, theory construction kept manual.
- 151 literature sources assembled
A qualitative corpus drawn from prior research on identity, social media, and propaganda.
- 2Processed with Evidano
Themes were surfaced from the corpus and turned into candidate categories.
- 3Categories carried into the theory
The formulated categories were included in the constructed theory and its modelling.
- 4Inductive theory development done manually
The grounded theory process itself remained a methodical, human-led exercise.
“The sample of literature sources included 51 items, and their processing was carried out with the help of AILYZE an AI-powered solution for grounded theory, with formulation of categories, which were further included in the constructed theory and modelling.”
“The application automatically finds and extracts important themes from qualitative data using natural language processing (futher – NLP), allowing researcher to quickly identify potential categories and concepts.”
“While AILYZE was employed for concentrating on the more complex and interpretive facets of developing theories, a methodical process for inductively developing theories from qualitative evidence was carried out manually, as it is implied in grounded theory.”
Categories and concepts surfaced from the corpus, ready for the researcher to test.
The same analysis applies to published sources, reports, and documents.
The study names the tool and states exactly which step it supported.
Published open access in a Scopus-indexed journal issued on behalf of Alexandru Ioan Cuza University of Iași.