Algorithms of Displacement: Emotional and Rhetorical Responses to AI-Driven Job Loss in Digital Public Discourse

As AI increasingly encroaches upon domains of human labor, public discourse has become a vital space for articulating and contesting its social implications. This study investigates how users on the X platform (formerly Twitter) respond emotionally and rhetorically to the prospect of AI-induced job displacement. Drawing on a dataset of 1,518 posts collected during key automation-related events, the research employs a mixed-methods approach combining Latent Dirichlet Allocation (LDA) topic modeling, machine-assisted emotion classification, and grounded rhetorical analysis. Findings reveal that user discourse is structured around ten recurring thematic clusters, each exhibiting distinct emotional profiles ranging from anger and fear to irony and hope. Users frequently engage in adversarial framing, ironic subversion, techno-messianism, and appeals to human exceptionalism to navigate and narrate their experiences with automation. These discursive strategies function not merely as expressions of individual opinion but as mechanisms for constructing collective identity, negotiating institutional trust, and shaping public imaginaries about technology and labor. The study highlights the symbolic and affective dimensions of technological disruption, arguing that digital platforms serve as critical arenas for the ongoing negotiation of meaning, power, and belonging in an increasingly automated society.