{"doi":"10.1002/asi.23347","title":"A lead‐lag analysis of the topic evolution patterns for preprints and publications","abstract":"<jats:p>This study applied <jats:styled-content style=\"fixed-case\">LDA</jats:styled-content> (latent <jats:styled-content style=\"fixed-case\">D</jats:styled-content>irichlet allocation) and regression analysis to conduct a lead‐lag analysis to identify different topic evolution patterns between preprints and papers from <jats:styled-content style=\"fixed-case\">arXiv</jats:styled-content> and the <jats:styled-content style=\"fixed-case\">W</jats:styled-content>eb of <jats:styled-content style=\"fixed-case\">S</jats:styled-content>cience (<jats:styled-content style=\"fixed-case\">WoS</jats:styled-content>) in astrophysics over the last 20 years (1992–2011). Fifty topics in <jats:styled-content style=\"fixed-case\">arXiv</jats:styled-content> and <jats:styled-content style=\"fixed-case\">WoS</jats:styled-content> were generated using an <jats:styled-content style=\"fixed-case\">LDA</jats:styled-content> algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that <jats:styled-content style=\"fixed-case\">arXiv</jats:styled-content> and <jats:styled-content style=\"fixed-case\">WoS</jats:styled-content> share similar topics in a given domain, but differ in evolution trends. Topics in <jats:styled-content style=\"fixed-case\">WoS</jats:styled-content> lose their popularity much earlier and their durations of popularity are shorter than those in <jats:styled-content style=\"fixed-case\">arXiv</jats:styled-content>. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.</jats:p>","journal":"Journal of the Association for Information Science and Technology","year":2015,"id":8779,"datarank":0.44166584687496613,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.0,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0793,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2015-04-02","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":34543,"name":"Xianlei Dong","orcid":"0000-0003-4090-8729","position":1,"is_corresponding":false},{"id":75520,"name":"Chenwei Zhang","orcid":"0000-0002-0488-4603","position":2,"is_corresponding":false},{"id":431,"name":"Timothy D. Bowman","orcid":"0000-0003-0247-4771","position":3,"is_corresponding":false},{"id":31284,"name":"Ying Ding","orcid":"0000-0001-5581-3058","position":4,"is_corresponding":false},{"id":23976,"name":"Staša Milojević","orcid":null,"position":5,"is_corresponding":false},{"id":3269,"name":"Chaoqun Ni","orcid":"0000-0002-4130-7602","position":6,"is_corresponding":false},{"id":23344,"name":"Erjia Yan","orcid":"0000-0002-0365-9340","position":7,"is_corresponding":false},{"id":228,"name":"Vincent Larivière","orcid":"0000-0002-2733-0689","position":8,"is_corresponding":false},{"id":75519,"name":"Beibei Hu","orcid":"0000-0002-6396-3854","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}