How are topics born? Understanding the research dynamics preceding the emergence of new areas

Abstract

The ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the 'parents' of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise.

Augur

“…In our previous work [7], we showed that the emergence of new research topics is correlated with specific dynamics of already established topics, paving the way to the detection of topics at their embryonic stage. In particular, using a sample of three million papers, we compared the sections of the topic cooccurrence graphs, where new research areas are about to emerge, with a control group of subgraphs associated with established topics.…”

“…In our recent study [7], we highlighted the existence of an embryonic phase, which predates the initial stage. In this phase, a topic has not yet been explicitly labelled and recognized by a research community, but it is already taking shape and affecting the research landscape, e.g., by fostering new collaborations between previously distant research communities.…”

“…4) of two topics in a previous time interval. In this paper, we will use a time interval of five years, since this solution was already tested successfully in Salatino, et al [7]. Therefore, the evolutionary network of the year t will contain a snapshot of the interactions between topics in the year interval (t-4, t).…”

DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS

“…That investigation showed that the average length of the evolutionary chain is 4.5 years. This choice was also tested successfully by Salatino et al [5]. The statistics of the dataset are given in Table I.…”

“…For the reasons cited above and the fact that citationbased approaches fail to dig into the paper content which could lead to a more accurate trend detection, some emerging researchers [4], [5] have followed a particular direction in computational history which is the use of topic models to analyse the rise and fall of research topics and accordingly the progress of science.…”