Topic Modeling of Bot Preferences in Tweets from the COVID-19 Parliamentary Inquiry in Brazil
DOI:
https://doi.org/10.5433/1679-0375.2025.v46.52599Keywords:
natural language processing, social media analysis, text mining, automated accounts detection, information disseminationAbstract
Twitter is a microblogging social network that allows users to send and receive short messages in text and image format, being one of the most widely used platforms of its kind. Given its relevance in various aspects of contemporary society, including politics, many users operate automated profiles (bots) that post hundreds or even thousands of tweets. This study applies the Latent Dirichlet Allocation (LDA) technique to identify whether bot users show preference for any of the modeled topics. Data were collected via the Twitter API between April 18 and May 30, 2021, using the keywords "CPI" (Parliamentary Inquiry Committee) and "COVID," resulting in 459,145 tweets in Portuguese from 109,027 distinct users. These users were analyzed through the Pegabot platform, which estimates the probability of an account being a bot. After preprocessing, only the users with 100 or more tweets during the period were retained, resulting in 26,966 observations from 189 accounts. LDA identified four main Topics: 1 - Health Secretary; 2 - senator Renan Calheiros; 3 - president Jair Bolsonaro; and 4 - the government. In all topics, bot accounts posts were more frequent than human users posts, with Topic 1 having the lowest proportion of accounts classified as bots.
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Copyright (c) 2025 Gabriel Thompson Marques Arruda, Anderson Castro Soares de Oliveira, Lia Hanna Martins Morita, José Nilton da Cruz
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