Publicação
Crisis in Mexico: the effect of the president’s discourse on state-level government communication about Covid-19 on Twitter
| Resumo: | Abstract This paper analyzes the relationship between the Mexican President’s discourse on Covid-19 and the use of Twitter by state officials at the start of the pandemic, through content analysis and supervised machine learning. Analyzing all tweets by state-level agencies during the first 6 months of the pandemic, we found that accounts belonging to the ruling party tweeted consistently less about Covid, compared to the opposition. Furthermore, the social-distancing hashtags endorsed by the Health Department were underused by the party’s own officials. We hypothesized that the president’s skeptical discourse on Covid-19 had a chilling effect on party officials’ use of Twitter during this period. Two random forest machine learning models were trained using the president’s words as predictors not only of the officials’ political alignment, but also of the amount of Covid tweets they posted. The models proved reliable, and the words most significant for prediction are markedly indicative of populist rhetoric. This illustrates how populist discourse from heads of government can undermine communication between institutions and citizens. |
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| Autores principais: | Corona,Antonio |
| Assunto: | covid-19 populism twitter communication social media |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Fundação para a Ciência e Tecnologia |
| Idioma: | inglês |
| Origem: | SciELO Portugal |
| Resumo: | Abstract This paper analyzes the relationship between the Mexican President’s discourse on Covid-19 and the use of Twitter by state officials at the start of the pandemic, through content analysis and supervised machine learning. Analyzing all tweets by state-level agencies during the first 6 months of the pandemic, we found that accounts belonging to the ruling party tweeted consistently less about Covid, compared to the opposition. Furthermore, the social-distancing hashtags endorsed by the Health Department were underused by the party’s own officials. We hypothesized that the president’s skeptical discourse on Covid-19 had a chilling effect on party officials’ use of Twitter during this period. Two random forest machine learning models were trained using the president’s words as predictors not only of the officials’ political alignment, but also of the amount of Covid tweets they posted. The models proved reliable, and the words most significant for prediction are markedly indicative of populist rhetoric. This illustrates how populist discourse from heads of government can undermine communication between institutions and citizens. |
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