Discovering weak signals of emerging topics with a triple-dimensional framework

In the rapidly evolving landscape of innovation, the early identification of emerging topics is crucial across diverse research domains. This study views weak signals as the preliminary stage of emerging topics and constructs an innovative weak signal triple-dimensional analytical framework to discern nascent emerging topics. The framework uses triads to represent signals by constructing keyword citation networks and establish a collection of novel signals through network topology analysis. Weak signals are subsequently identified by examining the visibility, diffusion and social influence of signals with time-weighted attributes. An altmetrics indicator is employed to formally measure the social influence of weak signals from the perspective of public perception. We apply the proposed framework to the field of gene editing, and the outcomes of literature analysis and dynamic validation substantiate the efficacy of our approach. Compared to related methods, our framework demonstrates a more nuanced ability to distinguish between various signals, identifying more weak signals and research topics with increased potential for social impact. This research provides valuable insights for strategic decision-making, innovation management, and future foresight.