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  4. U.S. Political Debates in Multilingual Online Spaces: A Computational Analysis of discourse quality across English and Spanish News media

U.S. Political Debates in Multilingual Online Spaces: A Computational Analysis of discourse quality across English and Spanish News media

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The 2020 election in the United States was historic for many reasons. One of the largest turnouts in recent history, the 2020 saw over half of eligible Hispanics cast their ballots, a historic first for the largest minority in the United States. Against this backdrop, this bilingual project seeks to understand the audience engagement strategies of Spanish- and English-language media organizations on Facebook driven by the following question: how can news organizations stimulate a quality discourse and participation? The analysis explores contextual factors that contribute to the quality of online discussions during the 2020 U.S. primary debates, with a particular focus on the consequences of news coverage and news commentors. State-of-the-art machine-learning models were developed in English and Spanish languages to analyze a large corpus of comments posted on news medias’ Facebook pages in response to the pre- and post-debate coverage. First, we put to rest apprehensions that strategic game reporting style is a deterrent to news audience online engagement and discussion quality. Second, addressing previous fears about the undesired democratic outcomes of uncivil talk, our data suggest that uncivil language can coexist with rational discourse in user comments, although this relationship is not pervasive in debate-related discussions. Yet, findings highlight important cultural differences when it comes to news engagement on social media and the importance of multilingual approaches to analyzing discourse quality in digital spaces.

Speaker:

Lindita Camaj, Ph.D.
Associate Professor & Director of graduate Studies, Valenti School of Communication, University of Houston

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