Echoes of Automation: How Bots Shaped Political Discourse in Brazil
- URL: http://arxiv.org/abs/2512.10749v1
- Date: Thu, 11 Dec 2025 15:40:10 GMT
- Title: Echoes of Automation: How Bots Shaped Political Discourse in Brazil
- Authors: Merve Ipek Bal, Diogo Pacheco,
- Abstract summary: Drawing on more than 315 million tweets posted from August 2018 to June 2022, we examine behavioural patterns, sentiment dynamics, and the thematic focus of bot- versus human-generated content.<n>Our analysis shows that bots relied disproportionately on retweets and replies, with reply activity spiking after the 2018 election.<n> Sentiment analysis indicates that bots maintained a narrower emotional tone, in contrast to humans, whose sentiment fluctuated more strongly with political events.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In an era where social media platforms are central to political communication, the activity of bots raises pressing concerns about amplification, manipulation, and misinformation. Drawing on more than 315 million tweets posted from August 2018 to June 2022, we examine behavioural patterns, sentiment dynamics, and the thematic focus of bot- versus human-generated content spanning the 2018 Brazilian presidential election and the lead-up to the 2022 contest. Our analysis shows that bots relied disproportionately on retweets and replies, with reply activity spiking after the 2018 election, suggesting tactics of conversational infiltration and amplification. Sentiment analysis indicates that bots maintained a narrower emotional tone, in contrast to humans, whose sentiment fluctuated more strongly with political events. Topic modelling further reveals bots' repetitive, Bolsonaro-centric messaging, while human users engaged with a broader range of candidates, civic concerns, and personal reflections. These findings underscore bots' role as amplifiers of narrow agendas and their potential to distort online political discourse.
Related papers
- Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
arXiv Detail & Related papers (2025-10-16T20:30:20Z) - Sleeper Social Bots: a new generation of AI disinformation bots are already a political threat [0.0]
"Sleeper social bots" are AI-driven social bots created to spread disinformation and manipulate public opinion.
Preliminary findings suggest these bots can convincingly pass as human users, actively participate in conversations, and effectively disseminate disinformation.
The implications of our research point to the significant challenges posed by social bots in the upcoming 2024 U.S. presidential election and beyond.
arXiv Detail & Related papers (2024-08-07T19:57:10Z) - Social bots sour activist sentiment without eroding engagement [0.0]
We find that bots exert a greater influence on human behavior than vice versa during heated online periods.
Political astroturfing bots increase activity, whereas other bots decrease it.
Despite the seemingly minor impact of individual bot encounters, the cumulative effect is profound due to the large volume of bot communication.
arXiv Detail & Related papers (2024-03-19T16:58:45Z) - BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline [47.61306219245444]
Twitter has become a target for bots and fake accounts, resulting in the spread of false information and manipulation.<n>This paper introduces a semi-automatic machine learning pipeline (SAMLP) designed to address the challenges associated with machine learning model development.<n>We develop a comprehensive bot detection model named BotArtist, based on user profile features.
arXiv Detail & Related papers (2023-05-31T09:12:35Z) - The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - You are a Bot! -- Studying the Development of Bot Accusations on Twitter [1.7626250599622473]
In the absence of ground truth data, researchers may want to tap into the wisdom of the crowd.
Our research presents the first large-scale study of bot accusations on Twitter.
It shows how the term bot became an instrument of dehumanization in social media conversations.
arXiv Detail & Related papers (2023-02-01T16:09:11Z) - Bots don't Vote, but They Surely Bother! A Study of Anomalous Accounts
in a National Referendum [1.5609988622100526]
We present a characterization of the discussion on Twitter about the 2020 Chilean constitutional referendum.
The characterization uses a profile-oriented analysis that enables the isolation of anomalous content using machine learning.
We measure how anomalous accounts (some of which are automated bots) produce content and interact promoting (false) information.
arXiv Detail & Related papers (2022-03-08T15:02:51Z) - Identification of Twitter Bots based on an Explainable ML Framework: the
US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data.
Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm.
Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z) - EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments [75.11753644302385]
Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation partner.
We propose a method based on a transformer pretrained language model (T5)
We evaluate our model on the EmpatheticDialogues dataset using both automated metrics and human evaluation.
arXiv Detail & Related papers (2021-10-30T19:04:48Z) - CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement
Learning [60.348822346249854]
This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns.
We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning.
To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
arXiv Detail & Related papers (2021-10-08T07:44:47Z) - Reaching the bubble may not be enough: news media role in online
political polarization [58.720142291102135]
A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations.
This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
arXiv Detail & Related papers (2021-09-18T11:34:04Z) - A Decade of Social Bot Detection [0.9137554315375922]
In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media.
What strategies should we enforce in order to stop this social bot pandemic?
What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010.
arXiv Detail & Related papers (2020-06-23T13:46:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.