2026-10-06T19:50:27Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34045641 2026-10-01T12:26:57Z category_29140 item_type_3 month_year_10_2026

Dataset of article: Mapping Themes in News Content: A Comparison of Expert Judgment and Science Mapping Techniques Anonimous (8209959) Organisation of information and knowledge resources Thematic analysis text mining science mapping SciMAT journalism news gender <h2 dir="ltr">In social science research, the analysis of large volumes of textual data poses a challenge for contemporary information management systems. Although science mapping tools such as SciMAT have proven effective in bibliometric studies of scientific information, their applicability to content analysis in news corpora remains unexplored. Therefore, this study aims to assess the usefulness of science mapping techniques for identifying themes in news and comparing their automated results with an expert manual classification. Using a comparative methodological design, a corpus of 316 articles on gender equality published in Spanish digital media during the first half of 2024 was analysed. In the first phase, an expert qualitative and quantitative content analysis, validated by specialised journalists, identified four major thematic blocks: “gender inequalities and violence”; “feminist movements and theories”; “the political, judicial, and legislative agenda on equality”; and “LGBTI+ rights”. SciMAT software was then used to process word co-occurrences and generate strategic maps based on centrality and density. The results demonstrate a high degree of complementarity between the two approaches: while the manual analysis provides narrative and contextual depth, the science mapping analysis offers a more disaggregated relational structure, identifying 17 thematic clusters as well as cluster-specific diagrams that clearly display the relationships among concepts and the thematic structure of the corpus. This study concludes that science maps are effective for working with much larger samples and reveal underlying semantic relationships and emerging themes.</h2><p></p> 2026-10-01T12:26:57Z Dataset Dataset 10.6084/m9.figshare.34045641.v1 https://figshare.com/articles/dataset/Dataset_of_article_Mapping_Themes_in_News_Content_A_Comparison_of_Expert_Judgment_and_Science_Mapping_Techniques/34045641 CC BY 4.0