Supporting data for: LLM-Assisted Keymorph Analysis of Grammatical Case in RT's Israeli–Palestinian Conflict Coverage (doi:10.18710/YTSGDM)

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Supporting data for: LLM-Assisted Keymorph Analysis of Grammatical Case in RT's Israeli–Palestinian Conflict Coverage

doi:10.18710/YTSGDM

DataverseNO

2026-05-15

1

Lu, Tingting, 2026, "Supporting data for: LLM-Assisted Keymorph Analysis of Grammatical Case in RT's Israeli–Palestinian Conflict Coverage", https://doi.org/10.18710/YTSGDM, DataverseNO, V1

Supporting data for: LLM-Assisted Keymorph Analysis of Grammatical Case in RT's Israeli–Palestinian Conflict Coverage

doi:10.18710/YTSGDM

Lu, Tingting (https://ror.org/00jdr0662)

Beijing Foreign Studies University

DataverseNO

The Tromsø Repository of Language and Linguistics (TROLLing)

Lu, Tingting

TROLLing curator

Lu, Tingting

2026-04-21

https://doi.org/10.18710/YTSGDM

Arts and Humanities, Keymorph Analysis, grammatical case, cognitive linguistics, conflict discourse, Large Language Models (LLMs)

<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>

2023-10-07-2025-01-19

2023-10-07-2025-01-19

Russian Federation

corpus data

RT (<a href="https://russian.rt.com" title="URL" target="_blank">russian.rt.com</a>). Insubstantial parts of this source are reused in this dataset under exceptions and limitations to intellectual property protection set out in the <a href="https://lovdata.no/dokument/NL/lov/2018-06-15-40" title="URL" target="_blank">Norwegian Copyright Act</a> and <a href="https://eur-lex.europa.eu/eli/dir/1996/9" title="URL" target="_blank">EU Database Directive</a>.

Media Subcorpus of the Russian National Corpus (<a href="https://ruscorpora.ru/" title="URL" target="_blank">ruscorpora.ru</a>). Insubstantial parts of this source are reused in this dataset under exceptions and limitations to intellectual property protection set out in the <a href="https://lovdata.no/dokument/NL/lov/2018-06-15-40" title="URL" target="_blank">Norwegian Copyright Act</a> and <a href="https://eur-lex.europa.eu/eli/dir/1996/9" title="URL" target="_blank">EU Database Directive</a>.

<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>

Lu, T. LLM-Assisted Keymorph Analysis of Grammatical Case in RT’s Israeli-Palestinian Conflict Coverage. <i>Russian Linguistics</i> (accepted)

Lu, T. LLM-Assisted Keymorph Analysis of Grammatical Case in RT’s Israeli-Palestinian Conflict Coverage. <i>Russian Linguistics</i> (accepted)

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00_ReadMe.txt

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01_RT_data_headlines_raw.txt

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02_RT_top200.py

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03_RT_top200_words.txt

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04_RT_top20_word_frequency_chart.py

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05_RT_top20_word_frequency_chart.png

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06_RT_data_with_keywords_raw.txt

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07_RT_annotation_AI.py

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08_RT_data_annotated_AI.txt

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09_RT_annotation_statistics_AI.csv

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10_RT_recall_calculation.txt

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11_RT_sample_AI.txt

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12_RT_sample_human.txt

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13_RT_discrepancies_between_AI_and_human_annotations.csv

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14_RT_data_annotated_reviewed.txt

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15_RT_annotation_statistics_human.csv

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16_keyword_Izrail_ruscorpora_content_4000.csv

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17_keyword_CAXAL_ruscorpora_content_2618.csv

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18_keyword_Gaza_ruscorpora_content_4000.csv

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19_keyword_XAMAS_ruscorpora_content_4000.csv

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20_keyword_Palestina_ruscorpora_content_4000.csv

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21_keyword_Livan_ruscorpora_content_4000.csv

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22_keyword_Iran_ruscorpora_content_4000.csv

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23_keyword_Xezbolla_ruscorpora_content_20 .csv

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24_keyword_USA_ruscorpora_content_4000.csv

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25_keyword_ООN_ruscorpora_content_4000.csv

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26_keyword_Rossija_ruscorpora_content_4000.csv

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27_REF_Prompt_AI_annotate.txt

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28_REF_AI_annotated.txt

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29_REF_annotation_statistics_AI.csv

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30_REF_samples_AI_annotated.txt

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31_REF_discrepancies_between_AI_and_human_annotations.csv

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32_REF_annotation_statistics_human.csv

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33_RT_chi_square_keyword_analysis.py

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34_RT_keywords_analysis_report.pdf

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35_RT_keywords_grammatical_case_standardized_residuals_precise.csv

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36_RT_grammatical_case_standardized_residuals_heatmap.py

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37_RT_grammatical_case_standardized_residuals_heatmap.png

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38_REF_Log-Likelihood.py

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39_REF_Log-Likelihood.csv

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40_Identification_ KMA_LL_SR.csv

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41_Primary_Secondary_Keymorphs_Viz.py

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42_Fig_Primary_Keymorphs_Viz.png

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43_Fig_Secondary_Keymorphs_Viz.png

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