What can software tell us about political candidates
A critical analysis of a computerized method for political discourse
Datos Bibliográficos
| ID | 6084751 |
|---|---|
| Autores | Sara E N Kanga (0000-0003-1564-2244) |
| Año | 2014 |
| Volumen | 13 |
| Número | 1 |
| Páginas | 77-97 |
| Fecha de publicación | 2014-05-15 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Language and Politics (JOURNAL) |
| Identificadores de la revista | ISSN: 1569-2159 • E-ISSN: 1569-9862 |
| Editorial | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/jlp.13.1.04kan |
| OpenAlex | W2055911524 |
| Idioma | EN |
| Citas recibidas | 5 |
| Referencias citadas | 20 |
This study evaluates a computerized text analysis program, Linguistic Inquiry and Word Count (LIWC), by investigating the relationship between the discourse and personalities of presidential and vice presidential candidates in the 2008 presidential election in the United States. Analyses of speech samples (N = 141) from Barack Obama, Joe Biden, John McCain, and Sarah Palin were conducted using LIWC. The results show that in the context of political speech events, such as media interviews, political candidates make unique linguistic choices, which may be interpreted as displaying distinct personality traits. Yet, despite the statistical significance of the results, there are salient limitations of utilizing computerized methodologies to analyze political speech events, such as the limited interpretative capacity of the software to understand pragmatic and contextual language use
Context (archaeology · Linguistics · Media studies · Personality · Personality psychology · Political science · Politics · Presidential system · Salient · Sociology · Authorship Attribution and Profiling · Computer Science · Discourse Analysis in Language Studies · Psychology · Sentiment Analysis and Opinion Mining · Artificial Intelligence · History · Law · Social Psychology
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Change
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| Obras citantes distintas | 5 |
|---|---|
| Citas por año | 0,56 |
| Intervalo de citas | 2017 - 2025 (9) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 5 |