Political Disaffection and Toxicity on X A Proposal to Classify Electoral Hashtags

Abstract

This article outlines a conceptual and methodological approach for the discursive detection of political disaffection on Twitter (now X) in electoral contexts, based on toxicity classification. This pilot study predominantly employs a quantitative approach to analyze the trend of messages deemed toxic within the hashtag #Elecciones2022, from a dataset of 115,493 downloaded tweets. These tweets pertain to contexts related to Costa Rica, Colombia, Spain, Mexico, and Peru. The corpus was subjected to automated classification using the Perspective tool, followed by manual classification of a sample's content according to the type of disaffection. Understood as an emotional and negative discursive framework towards the political system, this case proposes categories of analysis such as disaffection towards political actors, democratic institutions, cultural traits, or democracy itself, amidst Spanish-language conversations on X that contextualize electoral campaigns. The discussion suggests the potential of the social network X for detecting country-specific disaffection during electoral periods through automatic toxicity classification. However, the sample differences in each country context necessitate methodological replications for conclusive insights. A long-term longitudinal review could contribute to understanding the political culture of specific contexts, which can be leveraged in future research to begin filling gaps with new perspectives on approaches like the spiral of cynicism, focusing on digital news construction, hate, toxicity, and disaffection.