Rhetoric of parliamentary disinformation on Twitter

 Eva Campos-Domínguez, Valladolid (Spain)

 Marc Esteve-Del-Valle, Groningen (Netherlands)

 Cristina Renedo-Farpón, Valladolid (Spain)

https://doi.org/10.3916/C72-2022-04

Keywords

Disinformation, Twitter, MPs, political communication, COVID-19, social minorities

Abstract

Democracy is based on individuals’ ability to give their opinions freely. To do this, they must have access to a multitude of reliable information sources, and this greatly depends on the characteristics of their media environments. Today, one of the main issues individuals face is the significant amount of disinformation circulating through social networks. This study focuses on parliamentary disinformation. It examines how parliamentarians contribute to generating information disorder in the digital public space. Through an exploratory content analysis ? a descriptive content analysis of 2,307 messages posted on Twitter accounts of parliamentary spokespeople and representatives of the main list of each political party in the Spanish Lower House of Parliament ? we explore disinformation rhetoric. The results allow us to conclude that, while the volume of messages shared by parliamentarians on issues susceptible to disinformation is relatively low (14% of tweets), both the themes of the tweets (COVID-19, sex-based violence, migrants or LGBTI), as well as their tone and argumentative and discursive lines, contribute to generating distrust through institutional criticism or their peers. The study deepens current knowledge of the disinformation generated by political elites, key agents of the construction of polarising narratives.

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Fundref

Project funded by the Fundación BBVA, 2020 Leonardo Scholarship for Cultural Researchers and Creators: ”Parliament, bots and disinformation: ecology of strategies and practices in social networks”. Fundación BBVA is not responsible for the opinions, observations and content included in the project, which are entirely those of the authors’.

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Technical information

Received: 20-12-2021

Revised: 13-01-2022

Accepted: 02-03-2022

OnlineFirst: 15-05-2022

Publication date: 01-07-2022

Article revision time: 24 days | Average time revision issue 72: 31 days

Article acceptance time: 72 days | Average time of acceptance issue 72: 75 days

Preprint editing time: 147 days | Average editing time preprint issue 72: 150 days

Article editing time: 192 days | Average editing time issue 72: 195 days

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