10.18710/YTSGDM Lu, Tingting Tingting Lu https://orcid.org/0009-0008-0744-9894 https://ror.org/00jdr0662 DataverseNO 2026 Arts and Humanities Keymorph Analysis grammatical case cognitive linguistics conflict discourse Large Language Models (LLMs) Beijing Foreign Studies University The Tromsø Repository of Language and Linguistics (TROLLing) The Tromsø Repository of Language and Linguistics (TROLLing) Lu, Tingting Beijing Foreign Studies University TROLLing curator TROLLing 2026-04-21 2026-05-15 2023-10-07/2025-01-19 2023-10-07/2025-01-19 corpus data 10.18710/YTSGDM/NXBVBT 10.18710/YTSGDM/O4F8RY 10.18710/YTSGDM/VLRWTL 10.18710/YTSGDM/JWNF25 10.18710/YTSGDM/AQVEAS 10.18710/YTSGDM/HXNJE4 10.18710/YTSGDM/V1BDG3 10.18710/YTSGDM/CYHHUD 10.18710/YTSGDM/8IV6O4 10.18710/YTSGDM/YIIGBA 10.18710/YTSGDM/SKA16O 10.18710/YTSGDM/DAJ0ZW 10.18710/YTSGDM/RAMGTF 10.18710/YTSGDM/MMTICX 10.18710/YTSGDM/NHB5RZ 10.18710/YTSGDM/948IBZ 10.18710/YTSGDM/Y7LFBG 10.18710/YTSGDM/6HZ3HN 10.18710/YTSGDM/3IFJHQ 10.18710/YTSGDM/PCXJTZ 10.18710/YTSGDM/Y670WK 10.18710/YTSGDM/LWC4LS 10.18710/YTSGDM/4P9XTY 10.18710/YTSGDM/KXB682 10.18710/YTSGDM/VK3XMR 10.18710/YTSGDM/QHOB4M 10.18710/YTSGDM/6NSYIK 10.18710/YTSGDM/NPX0WZ 10.18710/YTSGDM/38Y0R1 10.18710/YTSGDM/SUW7RD 10.18710/YTSGDM/RDX209 10.18710/YTSGDM/JJ5HJE 10.18710/YTSGDM/LUADLQ 10.18710/YTSGDM/6BLIN3 10.18710/YTSGDM/14ARL6 10.18710/YTSGDM/4ANCGY 10.18710/YTSGDM/CJ8RMJ 10.18710/YTSGDM/LUK1WW 10.18710/YTSGDM/5QPNFN 10.18710/YTSGDM/RIIP2W 10.18710/YTSGDM/DB3GWR 10.18710/YTSGDM/LPKJ5D 10.18710/YTSGDM/CPHARP 10.18710/YTSGDM/IQXJAZ 1415234 8890 283282 137221 9309 283398 1047232 17262 2503756 1389964 12415599 10811 1157309 5992 881 639 263870 1169 25079 3316 726 13367 2532741 2585913 1650893 2444077 2562803 2611567 2593764 12289 2701340 2506876 2633250 5149 32090 924 85912 1809 945 10351 876536 9996 445284 861886 text/plain text/x-python text/plain image/png text/x-python text/plain text/plain text/plain text/plain text/plain text/plain text/x-python text/plain text/x-python text/x-python text/plain application/pdf text/comma-separated-values text/plain text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/plain text/comma-separated-values text/comma-separated-values text/comma-separated-values text/comma-separated-values text/x-python image/png text/x-python image/png image/png 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. <b>Dataset description:</b> <p>The dataset for this study supports a Keymorph Analysis of grammatical cases in Russian-language news headlines concerning the the 2023-2025 Israeli-Palestinian conflict, collected from RT's official news website.</p> <p>The dataset comprises four main components:</p> <ol> <li>Raw Headlines and Filtered Corpus: This component includes the initial collection of Russian-language headlines from RT (2023-10-07 to 2025-01-19) and the subsequently filtered corpus of 8,757 distinct headlines containing specified keywords related to the conflict (e.g., 'Israel', 'Palestine', 'Gaza', 'Hamas').</li> <li>Reference Corpus: The reference corpus was constructed from the National Media Subcorpus of the Russian National Corpus (RNC).</li> <li>Annotated Corpus of Grammatical Cases: This core component features the grammatical case annotations for 11 identified target keywords across the corpus. The annotations were generated using an LLM (ChatGPT-5 mini API) with a 20% human-reviewed and corrected sample integrated into the final dataset to ensure high quality and accuracy.</li> <li>Derived Analytical Data and Visualizations: This includes statistical summaries of keyword frequencies and grammatical case distributions, standardized Pearson residual values and log-likelihood (LL) ratio values crucial for keymorph identification, and various visualizations such as word frequency charts and residual heatmaps, all derived from the annotated corpus to support the keymorph analysis.</li> </ol> <b>Related article abstract:</b> <p>This study applies and extends Keymorph Analysis (KMA) with cognitive linguistic theory to investigate the representation of the Israeli–Palestinian conflict in Russia Today (RT)’s Russian-language headlines. Unlike traditional keyword analysis, which primarily focuses on lexical content, KMA reveals underlying narrative orientations by examining how systematic morphosyntactic choices contribute to the construal of participant roles. Our approach integrates three analytical layers: (1) a Quantitative Layer that identifies statistically significant keymorphs using a novel dual-reference framework (Standardized Residuals for internal distinctiveness and Log-likelihood tests against a broad reference corpus) via LLM-enhanced annotation (98.58% accuracy); (2) a Contextual Analysis Layer that maps these grammatical patterns to their specific lexical and semantic environments through corpus-assisted analysis; and (3) a Cognitive-Semantic Interpretation Layer grounded in the cognitive-semantic networks of the Russian case system. Through this integrated analysis, we identify a core-periphery hierarchy in case usage, revealing three contrastive cognitive schemas: military agents vs. humanitarian space, active entities vs. constrained subjects, and external dominance vs. regional passivity. Ultimately, this study provides a scalable, LLM-enhanced methodology for analyzing morphologically rich languages, advancing our understanding of how grammatical case assignment functions as a systematic mechanism for organizing participant positioning and constructing divergent narrative framings.</p> Russian Federation