How to use AI to analyze the citation influence and popularity of papers?

Artificial intelligence automates citation analysis by processing vast academic literature, detecting patterns in citation networks and contextual relevance beyond simple counts. This approach enables scalable, multidimensional assessment of scholarly impact. Key methodologies involve natural language processing to extract citation contexts and sentiment, coupled with network analysis techniques identifying influential nodes using centrality metrics like PageRank or HITS algorithms. Machine learning models, including graph neural networks, predict future citation trajectories or detect anomalies. Essential prerequisites encompass access to comprehensive bibliographic databases and preprocessing pipelines addressing data quality issues, such as author disambiguation or inconsistent metadata. Implementation begins with gathering structured citation data via APIs from sources like Crossref or Scopus. AI algorithms then analyze textual features in citing documents and construct citation graphs to quantify influence pathways. Applications include identifying seminal works for literature reviews, assessing researcher impact beyond journal metrics, and benchmarking institutional research performance, thereby supporting strategic funding decisions and scientific progress tracking.