We show that the Botometer scores are imprecise when it comes to estimating bots especially in a different language. To do so, we collected the Botometer scores for five datasets (three verified as bots, two verified as human n = 4,134) in two languages (English/German) over three months. Given its relevance for academic research and our understanding of the presence of automated accounts in any given Twitter discourse, we are interested in Botometer’s diagnostic ability over time. The bot classifier "Botometer" was successfully introduced as a way to estimate the number of bots in a given list of accounts and, as a consequence, has been frequently used in academic publications. The identification of bots is an important and complicated task.
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