Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media.
Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates co...
| Publicado en: | American Politics Research Vol. 50; no. 3; pp. 279 - 303 |
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| Autores principales: | , |
| Formato: | Artículo |
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Sage Publications Inc.
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=156290420&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 156290420 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 1532673X FY9 jtl: American Politics Research issn: 1532673X maglogo: Y pubinfo: dt: May2022 vid: 50 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 156290420 10.1177/1532673X211055676 ppf: 279 ppct: 24 formats: tig: atl: Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media. aug: au: Petkevic, Vladislav Nai, Alessandro affil: Faculty of Social and Behavioral Sciences, 1234 University of Amsterdam, Amsterdam, Netherlands Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Amsterdam, Netherlands su: United States. Congress. Senate Social media Elections Political campaigns Offensive behavior Automatic classification Classification Video coding sug: subj: Social media Elections Political campaigns Offensive behavior United States. Congress. Senate Automatic classification Classification Video coding keyword: incivility machine learning negative campaigning neural networks US Midterms incivility machine learning negative campaigning neural networks US Midterms ab: Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates competing in the 2018 Senate Midterms. The algorithm is able to identify the presence of political attacks (both in general, and specifically for character and policy attacks) and incivility. Due to the novel nature of the instrument, the article discusses the external and convergent validity of these measures. Results suggest that automated classifications are able to provide reliable measurements of campaign negativity. Triangulations with independent data show that our automatic classification is strongly associated with the experts' perceptions of the candidates' campaign. Furthermore, variations in our measures of negativity can be explained by theoretically relevant factors at the candidate and context levels (e.g., incumbency status and candidate gender); theoretically meaningful trends are also found when replicating the analysis using tweets for the 2020 Senate election, coded using the automated classifier developed for 2018. The implications of such results for the automated coding of campaign negativity in social media are discussed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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