In this paper, we propose and implement a systematic pipeline for the automatic classification of AI-related documents extracted from large-scale literature databases. This process results in the creation of an AI-related literature dataset named DeepDiveAI. The dataset construction pipeline integrates expert knowledge with the capabilities of advanced models, structured into two primary stages. In the first stage, expert-curated classification datasets are used to train a Long Short-Term Memory (LSTM) model, which performs coarse-grained classification of AI-related records from large-scale datasets. In the second stage, a large language model, specifically Qwen2.5 Plus, is employed to annotate a random 10% of the initially coarse set of classified AI-related records. These annotated records are subsequently used to train a Bidirectional Encoder Representations from Transformers (BERT) based binary classifier, further refining the coarse set to produce the final DeepDiveAI dataset. Evaluation results indicate that the proposed pipeline achieves both accuracy and efficiency in identifying AI-related literature from large-scale datasets.

Various stakeholders, such as researchers, government agencies, businesses, and research laboratories require a large volume of reliable scientific research outcomes including research articles and patent data to support their work. These data are crucial for a variety of application, such as advancing scientific research, conducting business evaluations, and undertaking policy analysis. However, collecting such data is often a time-consuming and laborious task. Consequently, many users turn to using openly accessible data for their research. However, these existing open dataset releases typically suffer from lack of relationship between different data sources and a limited temporal coverage. To address this issue, we present a new open dataset, the Intelligent Innovation Dataset (IIDS), which comprises six interrelated datasets spanning nearly 120 years, encompassing paper information, paper citation relationships, patent details, patent legal statuses, and funding information. The extensive contextual and extensive temporal coverage of the IIDS dataset will provide researchers and practitioners and policy maker with comprehensive data support, enabling them to conduct in-depth scientific research and comprehensive data analyses.

During the past decades, the term “social computing” has become a promising interdisciplinary area in the intersection of computer science and social science. In this work, we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project (DBLP), a representative computer science bibliography website. We have observed a series of trends in the development of social computing, including the evolution of the number of publications, popular keywords, top venues, international collaborations, and research topics. Our findings will be helpful for researchers and practitioners working in relevant fields.