What roles does AI play in citation analysis in academic papers?

Artificial intelligence significantly supplements traditional bibliometrics by automating citation metadata extraction, discovering latent relationships among publications, identifying emerging research patterns, and providing insights beyond manual capabilities. Its computational power enables scalable, nuanced analysis. Key applications include automated metadata extraction (authors, affiliations, cited works), sophisticated relationship mapping through citation networks, prediction of publication impact or emerging trends, assessment of influence beyond simple counts, and detection of interdisciplinary linkages. AI models require large, clean datasets and appropriate algorithm selection for validity. Integration with human expertise and critical evaluation remains crucial for robust interpretation. Applied roles encompass researcher profiling, literature recommendation, knowledge domain visualization, research trend identification, and evaluation of the evolution and convergence of scholarly fields. This enhances research discovery, strategic planning, and assessment of innovation diffusion, thereby accelerating knowledge dissemination and informing research funding or institutional policy.