Detecting research trends helps researchers and decision makers to promptly identify and analyze research topics. However, due to citation and publication delay, previous studies on trend analysis are more likely to identify ex-post trends. In this study, we employ author-defined keywords to represent topics and propose a simple, effective, and ex-ante approach, called author-defined keyword frequency prediction (AKFP), to detect research trends. More specifically, the proposed AKFP relies on the long short-term memory (LSTM) neural network. Four categories of features are proposed as input variables: Temporal feature, Persistence, Community size, and Community development potential. To verify the effectiveness and feasibility of the AKFP, we also proposed a simple but effective method to build a balanced and sufficient data set and conducted extensive comparative experiments, based on data extracted from the ACM Digital Library. Our empirical result confirms the feasibility of word frequency prediction by forecasting precision. Specifically, the short- and medium-term word frequency prediction achieved excellent performance, and the long-term word frequency prediction obtained acceptable prediction accuracy. In addition, we found that these proposed features have a significant but inconsistent impact on the AKFP. Specifically, the temporal feature is always an unignorable factor. The persistence has a strong correlation with the community size, and both are more important in the short- and medium-term prediction. In contrast, the community development potential is particularly significant in the long-term prediction.

检测研究趋势有助于研究人员和决策者迅速识别和分析研究主题。但是，由于引文和出版的延迟，以前对趋势分析的研究更有可能确定事后趋势。在这项研究中，我们使用作者定义的关键字来代表主题，并提出一种简单，有效且事前一种称为作者定义的关键字频率预测（AKFP）的方法来检测研究趋势。更具体地说，提出的AKFP依赖于长短期记忆（LSTM）神经网络。提出了四类特征作为输入变量：时间特征，持久性，社区规模和社区发展潜力。为了验证AKFP的有效性和可行性，我们还基于从ACM数字图书馆中提取的数据，提出了一种简单而有效的方法来构建平衡且足够的数据集，并进行了广泛的比较实验。我们的经验结果通过预测精度证实了词频预测的可行性。具体而言，短期和中期的词频预测取得了出色的成绩，长期词频预测获得了可接受的预测精度。此外，我们发现这些提议的功能对AKFP具有重大但不一致的影响。具体来说，时间特征始终是不可忽略的因素。持久性与社区规模有很强的相关性，两者在短期和中期预测中都更为重要。相反，在长期预测中，社区发展潜力特别重要。