Replication Data for: Measuring Political Narratives in African News Media: A Word Embeddings Approachdoi:10.7910/DVN/ZA6YENHarvard Dataverse2023-07-122Shen-Bayh, Fiona; Kitagawa, Risa, 2023, "Replication Data for: Measuring Political Narratives in African News Media: A Word Embeddings Approach", https://doi.org/10.7910/DVN/ZA6YEN, Harvard Dataverse, V2, UNF:6:mw45jNSEAX1mrMPOCahyXg== [fileUNF]Replication Data for: Measuring Political Narratives in African News Media: A Word Embeddings Approachdoi:10.7910/DVN/ZA6YENShen-Bayh, FionaKitagawa, RisaHarvard DataverseThe Journal of Politics DataverseShen-Bayh, FionaShen-Bayh, Fiona2023-06-22Social Sciencesinternational courtsmediatext-as-dataAfricaRecent advances in text-as-data provide new opportunities to document how elites shape public discourse on contentious issues. Using a novel word embeddings approach to measure elite-driven political narratives in local news, we analyze Kenyan newspaper coverage of the International Criminal Court's (ICC) prosecutions against domestic leaders. We train our embeddings on an original corpus of 5,292 Kenyan newspaper articles from 2007-2020 and identify significant changes in how local media vilifies the ICC before, during, and after major investigations. We find that as the case progressed, the ICC became more strongly associated with terms of bias and illegitimacy, an association which quickly dissipated after the Court terminated its last proceeding. Our approach illustrates the utility of text-based measures of political sentiment on contentious issues, with implications for research on media narratives, the effects of controversial jurisprudence on public discourse, and backlash against international institutions.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>ken_oped_data.tab529222text/tab-separated-valuesUNF:6:mw45jNSEAX1mrMPOCahyXg==article_id_15487.02839.5.1532.2987932574610.02836.4238473166595292.086.0UNF:6:AbjQrm1uuYD7XtedAZ7QNw==article_id_20.05292.085.05486.0.2838.51532.2987932574612835.423847316659UNF:6:8X/28nSWBIYz+6IJ+Z+uiQ==headlineUNF:6:2j94/z9L9PYvYP8RAOya3g==dateUNF:6:VkSE/CAkaJ7AUjTQ8NYg+A==publisherUNF:6:+XK/dAlNyhDCRoRBe8gbkg==bodyUNF:6:tx+F6ipVyv6rOulY9Eu8hg==urlUNF:6:hIvAmIFZ12D3tDnhvT1gjw==icc.1.01.00.01.01.00.05292.0UNF:6:Ic9g9L2rKCwOohevIkAynA==opinion.1.00.220982986767485432.05290.00.00.00.41494825571317256UNF:6:C7eqeGux/YTgcE5zwhLi1Q==author_nameUNF:6:QkNjIDjVVrnYn4MvfC/sYA==author_type2578.01.02714.0-2.28518791451729936.0-99.0.20.319338379573423UNF:6:TLa0Ek7d0C0Xims2YYAyqg==leaning5177.01.09.421683286017913115.0.3.00.7826086956521753-99.0UNF:6:r399R4zPh3EVbVl4uUs3qQ==noticcNaN.NaN5292.0NaN0.0NaNNaNUNF:6:G8BS5HbmgMjpgCZHe8Ax6Q==NotesUNF:6:VTQD3UanjgqBTG9OBOwrtA==ir_opinion0.0-8.4695652173913065177.0-99.0.28.06268726398049115.01.0UNF:6:CHrx+pUdtqDpXmMArzkLlw==ir_author_nameUNF:6:l990twVlWRmV9GMe7CgN7A==ir_author_type40.99223873324847-99.0.5281.011.01.0-16.181818181818186.0UNF:6:63eKvAllhSn/CmDsZkIpew==ir_leaning2.00.02.05291.01.02.02.0.UNF:6:WnS0hjKdqg/CoxIarY4SPQ==ir_noticc1.01.05290.00.02.01.0.1.0UNF:6:t8uBlIMGVeP382gPOPMUow==ir_notesNaN5292.0NaN.NaN0.0NaNNaNUNF:6:G8BS5HbmgMjpgCZHe8Ax6Q==year.2007.02020.01.92461127536739630.02013.02012.58956916099785292.0UNF:6:Ey2Qplr2Yvmyb6CCQRLL1w==monthUNF:6:9UXvttEbxHiPHfCoQfodog==codebook.rtfapplication/rtfglove.rdsapplication/gzipkhodakA.rdsapplication/gzipkitagawa_shenbayh_appendix_replication.Rtype/x-r-syntaxkitagawa_shenbayh_main_analysis_replication.Rtype/x-r-syntaxlocal_glove.rdsapplication/gziplocal_transform.rdsapplication/gzipOnline_Appendix_final.pdfapplication/pdfReadMe.txttext/plain