Colombia Internacional

Colomb. int. | eISSN 1900-6004 | ISSN 0121-5612

Framing the Cacerolazo: An Analysis of a Social Protest in Ecuador

No. 110 (2022-04-01)
  • Damián Fernández-Pedemonte
    Universidad Austral (Argentina)
  • Lorena Recalde
    Escuela Politécnica Nacional (Ecuador)
  • Gabriela Baquerizo-Neira
    Universidad Casa Grande (Ecuador)
  • Juan Carlos García
    Escuela Superior Politécnica del Litoral (Ecuador)

Abstract

Objective/Context: In the hybrid media system, Twitter users can propose alternative framings to those defined by media. In the context of the 2019 social protest in Ecuador, the framing confrontation was organized around hashtag #cacerolazo: different frames interpreted differently the meaning of the protest designated with this name. Methodology: The study analyzed 119,756 tweets containing trending hashtags related to the protest. The most frequent terms were analyzed, and it was observed that the word “cacerolazo” was framed and organized into three groups according to its context: 1) opposition to the government, which was the most popular; 2) support for the government, and 3) neutral. The framing defined by digital media for the same word was examined and categorized into groups for comparison; this allowed disambiguating the meaning of the word “cacerolazo” in the tweets. As a result, 67% of the tweets with the hashtag were in group 1 (opposition to the government). Thus, it was determined that, for the community considered in this study, the act of performing a cacerolazo retains its meaning of protest and it is not a call for peace. Conclusions: The framing of hashtag #cacerolazo was mostly promoted by users whose publications were against the government, and it quoted and criticized the framing established by the media. Originality: The word2vec model was used, which is an automated methodology that helps identify different frames defined by social media users during a certain event.

Keywords: Framing, public opinion, protests, hybrid media system, Twitter, digital media, data mining

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