Early Prediction of Academic Article Lifecycle Models Based on Multimodal Architecture
學年 112
學期 2
發表日期 2024-07-08
作品名稱 Early Prediction of Academic Article Lifecycle Models Based on Multimodal Architecture
作品名稱（其他語言）
著者 Chia-Ling Chang, Yi- Lung Lin and Yi-Hung Liu
作品所屬單位
出版者
會議名稱 The International Conference on Intelligent Science and Sustainable Development (ISASD 2024)
會議地點 東京，日本
摘要 The study of citation lifecycles in academic publications is crucial in scholarly research. Many studies use descriptive statistics or regression analyses to forecast citation outcomes, but they often don't fully combine textual data (like titles, abstracts, and keywords) with numerical data (such as impact factors and h-indexes). This research introduces an innovative multimodal model designed to predict early citation trajectories for scholarly articles, addressing this gap. We developed eight models to predict citations from the first to the eighth year based on 2017 data. Our lifecycle analysis shows that the model maintains high performance over multiple years, highlighting its robustness and adaptability. The results underscore the benefits of combining diverse data types for long-term predictive tasks, making our model a valuable tool for researchers and practitioners in Library and Information Science. This model significantly improves our ability to assess the early citation potential of academic papers, making it a valuable resource for researchers and policymakers in academic publishing. Additionally, to thoroughly explore bibliographic data, the study used LDA to investigate the topic distribution of library and information science publications in 2017.
關鍵字 Life Cycle of Scholarly Articles;Citation Time Window;Early Prediction;Multimodal learning;Deep learning
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20240708~20240711
通訊作者
國別 JPN
公開徵稿
出版型式
出處
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126191 )

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