Detecting research topic trends by author-defined keyword frequency

Wei Lu, Shengzhi Huang, Jinqing Yang, Yi Bu, Qikai Cheng, Yong Huang

Abstract: Highlights•This paper proposed author-defined keyword frequency prediction (AKFP) for detecting research topic trends and also offered the practical guidelines and other potential applications of the AKFP such as emerging topics identification.•The feasibility of short-, medium-, and long-term word frequency prediction was verified systematically, and AKFP achieved excellent performance in computer science field.•Four categories of proposed features (i.e. Temporal feature, Persistence, community size, and community development potential) have a significant but inconsistent impact on future keyword frequency.•We proposed a simple but effective method to build a balanced and sufficient training set for the AKFP, when encountering uneven data distribution (e.g. power-law).