Abstract
Artificial intelligence (AI) enhances human communication but also complicates information sharing in online political discourse. This paper empirically investigates AI-generated tweets’ impact on political discourse on Twitter. It analyzed 4,582 tweets on a contentious Canadian political topic in 2019, segmented into bot and human sub-corpora. Using WMatrix5, each corpus underwent semantic tagging across 21 discourse fields and 232 sub-classifications for statistical analysis. Spearman’s rank correlation coefficient showed strong positive correlations between bot-generated and human-generated tweets on March 14, 2019 (r(8) = 0.87, p = .001), March 28–29, 2019 (r(8) = 0.87, p = .001), and April 8–9, 2019 (r(8) = 0.91, p < .001). A linear regression model demonstrated that the bot-generated corpus significantly predicted variance in the semantic content of human-generated tweets, suggesting predictive influence of bot posts on human discourse.