Large-scale analysis of election tweets finds highest toxicity intensity in identity issues, harassment as the dominant harm type, partisan posts more toxic than neutral with issue-varying asymmetries, and toxic content driven by high-arousal negative emotions plus context-shaped moral foundations.
Törnberg, Best practices for text annotation with large language models, arXiv preprint arXiv:2402.05129 (2024)
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BERT embeddings encode narrative dimensions of time, space, causality, and character at the token level, as a linear probe achieves 94% accuracy versus 47% on variance-matched random embeddings, though unsupervised clusters do not align with these categories.
citing papers explorer
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Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions
Large-scale analysis of election tweets finds highest toxicity intensity in identity issues, harassment as the dominant harm type, partisan posts more toxic than neutral with issue-varying asymmetries, and toxic content driven by high-arousal negative emotions plus context-shaped moral foundations.
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Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction
BERT embeddings encode narrative dimensions of time, space, causality, and character at the token level, as a linear probe achieves 94% accuracy versus 47% on variance-matched random embeddings, though unsupervised clusters do not align with these categories.