A Mixed Approach for Aggressive Political Discourse Analysis on Twitter

Abstract

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an increment of the different tensions underlying politics, as has been reported after many other political and economical crises during the twentieth century. This article proposes the study of the political discourse, and its underlying tension, during Madrid’s elections (Spain) in May 2021 by using a mixed approach . To demonstrate if an aggressive tone is used during the campaign, a mixed methodology approach is applied: quantitative computational techniques, related to natural language processing, are used to conduct a first general analysis of the information screened; then, these methods are used for detecting specific trends that can be later filtered and analyzed using a qualitative approach (content analysis), which is also conducted to extract insights about the information found. The main outcomes of this study show that the electoral campaign is not as negative as perceived by the citizens and that there was no relationship between the tone of the discourse and its dissemination. The analysis confirms that the most ideologically extreme parties tend to have a more aggressive language than the moderate ones. The content analysis carried out using our methodology showed that Twitter is used as a sentiment thermometer more than as a way of communicating concrete politics.

Discurso político ante el multiculturalismo de la derecha española: un análisis comparado entre PP y Vox

“…El análisis a través de plataformas sociales en línea (OSP), a pesar de excluir medios tradicionales y otros formatos (p. ej., programas electorales), es relevante, dado que sus usuarios más activos e influyentes son los periodistas, políticos y sus partidarios y, por tanto, Twitter es una plataforma relevante en la configuración de la agenda pública (Torregrosa et al, 2023).…”

Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants

“…In contrast to generic sentiment analysis, stance detection aims to identify the public’s opinions in relation to a concrete target, such as different issues, topics, events, products, political candidates, etc. Torregrosa et al [ 53 ] study the political discourse on Twitter from a sentiment analysis and “campaign tone” perspective as applied to the 2021 Spanish local elections in the Autonomous Community of Madrid. The authors then perform an n-gram and hashtag analysis of the results in order to facilitate a qualitative analysis of the debate.…”

You are what your feeds makes you: A study of user aggressive behaviour on Twitter

“…Studies have shown that aggressive behavior is prevalent on Twitter (Kwak et al, 2010;Wu et al, 2011). Several studies have created annotated datasets of aggression in various languages, such as English (Kumar et al, 2018b,a;Bhattacharya et al, 2020;Kumar et al, 2020;Ali et al, 2023), Hindi (Kumar et al, 2018b,a;Bhattacharya et al, 2020;Kumar et al, 2020), Bengali (Bhattacharya et al, 2020;Kumar et al, 2020;Sharif and Hoque, 2022), Italian (Gattulli et al, 2022), Spanish (Torregrosa et al, 2022), Turkish (Balci and Salah, 2015), and Russian (Gordeev, 2016). These datasets were curated from social media platforms such as Facebook, Twitter, YouTube, and others.…”

“…The use of aggressive language on social media has been studied in various contexts. The various types of aggressive behaviors directed toward robots with AI enhancements has studied by Oravec (2023) Torregrosa et al (2022) found that ideologically extreme political parties tend to use more aggressive language. Pascual-Ferrá et al (2021) explored how aggressive language is used in the online discourse around wearing face masks during the COVID-19 pandemic.…”

“…Despite an upsurge in research on aggressive behavior on Twitter, the relationship between consuming aggressive content and user behavior remains unexplored. Previous research has primarily focused on aggression detection on social media platforms using various machine learning (Datta et al, 2020;Arroyo-Fernández et al, 2018;Gutiérrez-Esparza et al, 2019), deep learning models (Aroyehun and Gelbukh, 2018;Srivastava and Khurana, 2019;Kumari et al, 2021), as well as identifying aggression in multilingual (Sharif and Hoque, 2022;Kumari et al, 2021;Torregrosa et al, 2022) and multi-modal posts (Khandelwal and Kumar, 2020;Kumari and Singh, 2022) on social media. Apart from aggression detection, some sociological studies of aggressive behavior on social media exist (Vladimirou et al, 2021;Pascual-Ferrá et al, 2021).…”