Online political hostility is pervasive, yet it remains unclear how toxicity varies across campaign issues and political ideology, and what psychosocial signals and framing accompany toxic expression online. In this work, we present a large-scale analysis of discourse on X (formerly Twitter) during the five weeks surrounding the 2024 U.S. presidential election. We categorize posts into 10 major campaign issues and estimate the ideology of posts with a human-in-the-loop LLM-assist annotation process, detected harmful content using an LLM-based toxicity detection model, and then examined the psychological drivers of toxic content. We used these annotated data to examine how harmful content varies across campaign issues and ideologies, as well as how emotional tone and moral framing shape the toxicity in the election discussion. Our results show issue heterogeneity in both the prevalence and intensity of toxicity, especially the discussion on identity-related issues, such as Immigration and Racial & Gender Inequality, displays the highest toxicity intensity. As for specific harm categories, harassment was most prevalent and showed the most intensity across all divided issues, while hate concentrated in identity-centered debates. Additionally, partisan posts contained more harmful content than neutral posts, and that ideological asymmetries in toxicity vary by issue. In terms of psycholinguistic dimensions, we found that toxic discourse is dominated by high-arousal negative emotions, such as anger and fear. At the same time, left- and right-leaning posts often exhibit similar emotional profiles within the same issue domain, suggesting emotional mirroring. Similarly, partisan groups frequently rely on overlapping moral foundations but differ in emphasis, while issue context strongly shapes which moral foundations become most salient. These findings provide fine-grained account of toxic political discourse on social media and highlight the need for context-sensitive interventions that focus on high-risk issues and partisan conflict.

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