Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media

Vladislav Petkevic,Alessandro Nai

doi:10.1177/1532673x211055676

Abstract

Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates competing in the 2018 Senate Midterms. The algorithm is able to identify the presence of political attacks (both in general, and specifically for character and policy attacks) and incivility. Due to the novel nature of the instrument, the article discusses the external and convergent validity of these measures. Results suggest that automated classifications are able to provide reliable measurements of campaign negativity. Triangulations with independent data show that our automatic classification is strongly associated with the experts’ perceptions of the candidates’ campaign. Furthermore, variations in our measures of negativity can be explained by theoretically relevant factors at the candidate and context levels (e.g., incumbency status and candidate gender); theoretically meaningful trends are also found when replicating the analysis using tweets for the 2020 Senate election, coded using the automated classifier developed for 2018. The implications of such results for the automated coding of campaign negativity in social media are discussed.

Highlights

Several studies have assessed the presence of negativity in social media (e.g., Auter & Fine, 2016; Ceron & d’Adda, 2016; Evans et al, 2014; Evans & Clark, 2016; Gainous & Wagner, 2014; Gross & Johnson, 2016). These studies find confirmation that the main trends of strategic campaigning found for traditional techniques—for instance, that challengers tend to attack more than incumbents (Gainous & Wagner, 2014)—are found when looking at campaigning on social media
We introduce a neural network classifier that we trained to automatically annotate the tweets of candidates competing during the 2018 US Senate Midterms elections, in terms of the presence of political attacks
2014; Gross & Johnson, 2016), and broadly confirmed that the main trends of strategic campaigning found for traditional techniques—for instance, that challengers tend to attack more than incumbents (Gainous & Wagner, 2014)—are found when looking at campaigning on social media

Summary

Introduction

The public is increasingly hostile toward those they consider as their rivals (Iyengar et al, 2012; Iyengar & Westwood, 2015), antagonistic and aggressive political figures are on the rise across the globe (Nai & Martinez i Coma, 2019a), and attacks seem at times the very essence of election campaigns (Ansolabehere et al, 1994; Lau & Pomper, 2004). Strong evidence suggests that negative campaigning can be a detrimental force in modern democracies. Negative and harsh campaigns can reduce turnout and political mobilization, depress civic attitudes such as political efficacy and trust, foster apathy, and generally produce a “gloomier” public mood (Ansolabehere et al, 1994; Thorson et al, 2000; Yoon et al, 2005). Whether a positive or detrimental force, few would contest that negativity is a key component of contemporary electoral democracies

Methods

Results

Conclusion