Identifying the Political Tendency of Social Bots in Twitter Using Sentiment Analysis: A Use Case of the 2021 Ecuadorian General Elections

Quelal, Andres; Brito, Juan; Lomas, Mateo Sebastián; Camacho, Jean; Andrade, Argenis; Cuenca, Erick

doi:10.1007/978-3-031-18347-8_15

Cited by 3 publications

(1 citation statement)

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“…On the one hand, behavior-based ML approaches focus on analyzing the user features to detect bots on Twitter [22,23]. For instance, Alarfaj et al [24] use a Support Vector Machine and Neural Network techniques for this task.…”

Section: Detection and Analysis Techniquesmentioning

confidence: 99%

Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

Diaz,

Luzuriaga,

Salazar

et al. 2024

Preprint

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The 2023 Ecuadorian presidential elections, contested between Daniel Noboa and Luisa González, marked a significant event in the nation's democratic journey. Amidst the fervor of campaigns and political debates, the role of social media, particularly Twitter, emerged as a focal point of interest. This study delves into the Twitter activities of both candidates, aiming to discern the potential use of automated actors, commonly known as ''bots", in shaping online narratives and influencing public opinion. By employing state-of-the-art bot detection methodologies, we analyzed the tweet patterns, engagement metrics, and content dissemination strategies associated with the official accounts of the candidates. Our findings shed light on the extent and implications of bot-driven interactions, offering insights into the evolving dynamics of political communication in the digital age. The results underscore the importance of ensuring transparency and authenticity in online electoral campaigns, pivotal for preserving the sanctity of the democratic process.

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Section: Detection and Analysis Techniquesmentioning

confidence: 99%