Survey of 223 Reddit users and qualitative analysis of 2,248 posts shows certified developers frequently use online forums for privacy law advice, identifying key challenges and credibility assessment methods.
Törnberg, Best practices for text annotation with large language models, arXiv preprint arXiv:2402.05129 (2024)
3 Pith papers cite this work, alongside 23 external citations. Polarity classification is still indexing.
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Pith papers citing it
23
external citations · external index
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2026 3verdicts
UNVERDICTED 3representative citing papers
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.
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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The Role of Online Forums in Developer Understanding of Privacy Law -- A Reddit Case Study
Survey of 223 Reddit users and qualitative analysis of 2,248 posts shows certified developers frequently use online forums for privacy law advice, identifying key challenges and credibility assessment methods.
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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.