Guy Grossman, Stephanie Zonszein &ndash; University of Pennsylvania, USA
In a context of nativism and poor representation of immigrant-origin ethnic minori- ties, what is the reaction of the host society when immigrants succeed at integration in political institutions? Building on threat theory—which links minorities’ political power to hostility against minoritized groups—we argue that when they win political office, immigrants pose a threat to natives’ dominant position. This in turn triggers a hostile reaction from a violent-prone fringe, the mass public and the elites. We test these dynamics across the last four UK general elections, using hate crime police records, public opinion data, and text data from over 500,000 news articles from 350 na- tional and local newspapers. We identify the public’s hostile reactions with a regression discontinuity design that leverages close election results between minority-immigrant and dominant group candidates. Our findings suggest a public backlash against ethnic minority immigrants’ integration into majority settings.
News articles were extracted from Common Crawl, ethnic background of candidates is constructed by the authors, and constituency characteristics from 2001 and 2011 UK Decennial Census. [...] Then, to obtain the articles published by each of these newspapers, we looked up the URLs in Common Crawl (an open repository of web crawl data containing a snapshot of every web page at the moment of the crawl). Particularly in the Index for 2020-16 crawl, the most recent crawl at that moment. We retrieved the WARC (Web ARChive format) records for each crawled page from the newspaper, and extracted the pages’ HTML. From the HTML, we extracted the text, title, and byline using the Python package readabiliPy; the publication date using the Python library htmldate; the location by tokenizing the article with CoreNLP, and looking for tokens which match place names in the Index of Place Names in Great Britain, and mapping to the corresponding constituency. Figure D.1 presents the geographical coverage of all extracted articles across constituencies.¶ [...] 4.3 Media tone toward migrant groups¶ Data We use data from over 500,000 articles from 350 national, regional and local UK newspapers, covering the general elections from 2010–2019.⁸ This data is from Common Crawl, which is an open repository of web crawl data. We assume that an article refers to a candidate’s ethnic group when three conditions are met: 1) the publication date is on election day and up to 10 months after each general election⁹, 2) the article contains mentions of terms referring to the candidate’s country or nationality of origin, which are extracted with the named entity annotator of CoreNLP and 3) such mentions co-occur in the article with a mention referring to the candidate’s constituency. The constituency is extracted by tokenizing the article with CoreNLP and looking for tokens which match place names in the Index of Place Names in Great Britain, and mapping to the corresponding constituency. Overall, this data includes almost 150,000 mentions from 156 newspapers that meet these three conditions about the candidates’ group. [...] D Newspaper data, computation of media tone measures and validation of key elements Newspaper data We construct the dataset of newspaper articles using the following steps. To determine a comprehensive list of UK newspapers, we first identified a list of seed categories on Wikipedia (WP) (e.g. ’Category:Newspapers_published_in_England’), we took the recursive items of those categories (e.g. ’Category:Newspapers_published_in_England’ > ’Category:Newspapers_published_in_London’), we used WP article properties to filter out articles about non-newspapers (e.g. people, books), and we extracted the newspaper URLs from the WP Infobox using the Python package wptools. With this process we identified a list of UK newspapers URLs containing 337 newspapers in total. Then, to obtain the articles published by each of these newspapers, we looked up the URLs in Common Crawl (an open repository of web crawl data containing a snapshot of every web page at the moment of the crawl). Particularly in the Index for 2020-16 crawl, the most recent crawl at that moment. We retrieved the WARC (Web ARChive format) records for each crawled page from the newspaper, and extracted the pages’ HTML. From the HTML, we extracted the text, title, and byline using the Python package readabiliPy; the publication date using the Python library htmldate; the location by tokenizing the article with CoreNLP, and looking for tokens which match place names in the Index of Place Names in Great Britain, and mapping to the corresponding constituency. Figure D.1 presents the geographical coverage of all extracted articles across constituencies.