Research on the hysteresis effect of topic related evolution for emerging trends prediction
Ziqiang Liu
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Article Category: Research Papers
Published Online: Apr 04, 2025
Page range: 52 - 77
Received: Oct 30, 2024
Accepted: Mar 13, 2025
DOI: https://doi.org/10.2478/jdis-2025-0021
Keywords
© 2025 Ziqiang Liu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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.