Work

Research on the hysteresis effect of topic related evolution for emerging trends prediction

Year: 2025

Type: article

Abstract: ABSTRACT Purpose The study examines the synergy and hysteresis in the evolution of funding and its supported literature, depicts their temporal correlation mechanism, which aids in improving trend predictions. Design/methodology/approach The study uses the LDA model to identify topics in funding texts and supported papers. A cosine similarity algorithm was employed to estimate the nexus between topics and construct the topic evolution time series. Similarly, the hysteresis effect in topic evolution is analyzed based on topic popularity and content, leading to insights into their temporal correlation mechanism. Findings The study finds that fund and sponsored paper topics exhibit strong collaboration with a noticeable lag in evolution. The fund topics significantly influence sponsored paper topics after a two-year lag. Moreover, the lag effect is inversely proportional to the topic’s similarity. Research limitations We use the LDA model to determine the hysteresis effect in topic evolution despite its limitations in handling long-tail words and domain-specific vocabulary. Furthermore, the timing of the emergence of the focal topic in funds is undermined, affecting the findings. Practical implications These findings enhance the accuracy and scientific validity of trend prediction. Estimating and identifying patterns can help technology managers anticipate future research hotspots, supporting informed decision-making and technology management. Originality/value This study introduces a research framework to quantitatively and visually analyze the hysteresis effect, revealing the correlation and evolutionary patterns between fund research topics and their funded papers.

Source: Journal of Data and Information Science

DOI: 10.2478/jdis-2025-0021

Authors: Ziqiang Liu, Haiyun Xu, Lixin Yue, Zenghui Yue

Institutions: Nanjing Normal University, Shandong University of Technology, Jining Medical University

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Shandong Province

Award: G1878665083