{"id":1421,"date":"2019-05-13T09:00:26","date_gmt":"2019-05-13T13:00:26","guid":{"rendered":"https:\/\/commons.princeton.edu\/immigrationpolitics\/?p=1421"},"modified":"2019-05-13T09:00:26","modified_gmt":"2019-05-13T13:00:26","slug":"ny-14-media-content-analysis","status":"publish","type":"post","link":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/ny-14-media-content-analysis\/","title":{"rendered":"NY-14 Media Content Analysis"},"content":{"rendered":"

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Intro Slide<\/strong><\/p>\n

NY-14 is currently represented by Alexandria Ocasio-Cortez.\u00a0 On the right, she is pictured protesting the Trump administration immigration policies and child detention in Texas.<\/p>\n

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Slide #1<\/strong><\/p>\n

Congressional District NY-14 has a high, non-native, non-white population.\u00a0 21.6% of residents are naturalized foreign-born citizens, while 25.4% are non-U.S. citizens.\u00a0 By racial demographics, the district is comprised of 47.7% Latinx, 18% Asian, and 11.1% Black constituents.\u00a0 Academic literature suggests that, because of the high proportions of minority groups, NY-14\u00a0will have resources available to improve lives of immigrants.\u00a0 Specifically, Abrajano & Singh (2009) found that, when available, Spanish television news sources are statistically significantly more positive when speaking about immigration topics than their English counterparts.\u00a0 Because of NY-14\u2019s high minority population, the current study hypothesizes the following: H1) that the local Spanish daily newspaper will be more positive towards immigration than the local English daily newspaper; H2) that the Spanish coverage will focus more on humanitarian aid than the English source.\u00a0 These hypotheses are grounded in the research by Abrajano & Singh as more positive coverage may imply more focus on humanitarian aid rather than partisanship issues and border security.<\/p>\n

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Slide #2<\/strong><\/p>\n

One hundred articles were collected from each of the two local news sites — El Diario (Spanish) and the Daily News (English) —\u00a0 from the period of December 11,2018 – January 31,2019, coinciding with the government shutdown.\u00a0 An article was collected if it contained any of the following words: immigration, immigrant, border, wall, shutdown, security, or undocumented (English); or inmigrante, muro fronterizo, inmigraci\u00f3n, muro en la frontera, indocumentado, indocumentada, al cierre del gobierno, or seguridad (Spanish).\u00a0 Perfect translations for the Spanish terms often did not exist and needed to be constructed by a phrase or were necessarily extended to multiple genders if an adjective.\u00a0 In order to specify key terms and date ranges, articles were scraped by a Python script through Google News.\u00a0 The scrape was limited to 100 articles in each language due to a lack of computational power.\u00a0 To complete the analysis, word sentiments were obtained through the NRC Word-Emotion Association lexicon, which provides positive-negative ratings and emotional connotations of words in over 100 languages based in the original English lexicon.\u00a0 In order to determine article focus in categories of Partisanship, Security, and Humanitarian Aid, two words (pictured on the bottom right) were chosen for each category that acted as signals for article content.\u00a0 As with the search terms, \u201cRepublican\u201d was necessarily extended to \u201cRepublicano\u201d and \u201cRepublicana\u201d to account for gendered adjectives in Spanish.<\/p>\n

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Slide #3<\/strong><\/p>\n

Two-sample t-tests were run on the English and Spanish article content to determine if one source used more positive or negative language in speaking about immigration.\u00a0Both sentiments were tested because positivity and negativity are not necessarily mutually exclusive since many words in the lexicon are not associated with either sentiment and some words may carry both connotations.\u00a0 Results did not show a significant difference between sentiment values, so the null hypothesis that the language sources are not different in sentiment cannot be rejected and H1 is inconclusive.\u00a0 Graphs are pictured on the right, which show the top words used in each language for each sentiment.\u00a0 Although it is not a statistical test and general conclusions cannot be drawn, the results are interesting to interpret.\u00a0 For example, both language sources heavily used \u201cgovernment\u201d and \u201cpresident,\u201d but English positive words center around money and resources, while the Spanish words are about agreement and personal security (\u201cSeguro\u201d, \u201ccasa\u201d, \u201cconstruir\u201d).\u00a0 Visualizing the top used words also helps identify the limitations of this analysis and the NRC lexicon in particular.\u00a0 \u201cPresident,\u201d for example, while generally a positive term, could easily be seen as negative or neutral in the situation of the shutdown, depending on the political beliefs of the human interpreter.\u00a0 Likewise, \u201ccierre\u201d in Spanish is classified as positive; however, it means \u201cclosure,\u201d which is not positive in the context of immigration.\u00a0It is also interesting, and perhaps problematic in this context, that \u201cwhite\u201d is heavily used in the English source and that it is classified as positive.\u00a0 Future research should address these limitations by compiling an emotion lexicon specific to immigration through mass polling to avoid researcher bias.<\/p>\n

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Slide #4<\/strong><\/p>\n

Articles were classified into each focus by examining which category had the most terms per article.\u00a0For example, if an article had 30 Security terms, and only 10 each of Partisanship and Humanitarian Aid, it was classified as a Security article.\u00a0 Two-sample t-tests were also run to determine focus of each language.\u00a0Although the three categories are mutually exclusive, all three tests were run to determine which focuses were preferred, if any, in each language.\u00a0 There was no significant difference in Partisanship classification between sources; however, a significant difference was shown for both Security and Humanitarian Aid.\u00a0 El Diario articles were shown to be classified as Security at a statistically significantly higher rate than The Daily News articles.\u00a0 Dissimilarly, El Diario articles were classified as Humanitarian Aid focus at a significantly lower rate than The Daily News.\u00a0 These results hold even after applying a Bonferroni correction (m = 5, for each sentiment and focus test; p < 0.01).\u00a0That said, as pictured on the right, the English articles contained more focus terms for each category in general, with the average English article containing 8.48 Partisanship terms as compared to Spanish articles at 1.4 terms per article.\u00a0 These results are similar across focus categories as seen in the graph.\u00a0This difference may be due, in part, to imperfect translation of the terms.\u00a0 Although they were directly translated using crowd-sourced translation service (Linguee) and researcher language experience (Advanced Proficient), a direct translation may not have been appropriate in this instance, because the language may use other terms to signal the focus categories.\u00a0 Future research should consult native Spanish speakers and Spanish news writers to adequately translate focus terms.<\/p>\n","protected":false},"excerpt":{"rendered":"

Intro Slide NY-14 is currently represented by Alexandria Ocasio-Cortez.\u00a0 On the right, she is pictured protesting the Trump administration immigration policies and child detention in Texas. Slide #1 Congressional District NY-14 has a high, non-native, non-white population.\u00a0 21.6% of residents are naturalized foreign-born citizens, while 25.4% are non-U.S. citizens.\u00a0 By racial demographics, the district is […]<\/p>\n","protected":false},"author":1134,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[],"class_list":["post-1421","post","type-post","status-publish","format-standard","hentry","category-districts-3","post-preview"],"_links":{"self":[{"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/posts\/1421","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/users\/1134"}],"replies":[{"embeddable":true,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/comments?post=1421"}],"version-history":[{"count":2,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/posts\/1421\/revisions"}],"predecessor-version":[{"id":1428,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/posts\/1421\/revisions\/1428"}],"wp:attachment":[{"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/media?parent=1421"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/categories?post=1421"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/commons.princeton.edu\/immigrationpolitics-2019\/wp-json\/wp\/v2\/tags?post=1421"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}