12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

Authorship & Attribution

The Authorship & Attribution theme comprises 107 papers in this corpus published between 1997 and 2026. Work here is dominated by Empirical Study, Experimental, Position Paper. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study28 (26%)
  • Experimental16 (15%)
  • Position Paper14 (13%)
  • Conceptual13 (12%)
  • Benchmarking11 (10%)
  • Literature Review9 (8%)

Research domains

  • Research Integrity81 (76%)
  • Scholarly Infrastructure7 (7%)
  • Academic Writing6 (6%)
  • Peer Review3 (3%)
  • Human-AI Collaboration2 (2%)
  • Bias & Epistemic Risk1 (1%)

Frequent sub-topics

AI and non-human co-authorship in literary prizes, authorial power dynamics, AI-assisted creative writing · 1stylometric feature engineering for classical Arabic texts · 1AI detection tools and authorship attribution reliability · 1Distinguishing human-authored from AI-generated texts; emotion and personality in LLM outputs · 1LLM fingerprinting detection in non-English text · 1historiographical error in authorship of UNESCO Library Manifesto; use of generative AI to understand error perpetuation · 1fine-tuned RoBERTa classifier for AI-generated text detection with interpretability analysis · 1philosophical perspectives on AI-assisted journalism authorship · 1

Open research gaps

  • The authors identify the need for "future research to explore the implications of stylometric patterns in Classical Arabic in general and in Quranic texts in particular."
  • The authors identify gaps in understanding the robustness and potential biases of LLMs in automatic text scoring tasks. They note that while prior research focused on accuracy, "their robustness and p
  • The authors identify the need for further research to fully validate the effectiveness of LLM-generated impostors, scaling the approach to larger datasets, optimizing prompt design strategies, and red
  • The paper positions quote attribution as an emerging benchmark area for testing representational fairness in LLMs, suggesting this has been an understudied aspect of LLM evaluation.
  • Future research should investigate cognitive mechanisms of source evaluation, track longitudinal changes in AI content reception, and examine industry-specific applications with professional audiences

Representative papers

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