Exploring the Technical Knowledge Interaction of Global Digital Humanities: Three-decade Evidence from Bibliometric-based perspectives

Digital Humanities (DH) is an interdisciplinary field that integrates computational methods with humanities scholarship to investigate innovative topics. Each academic discipline follows a unique developmental path shaped by the topics researchers investigate and the methods they employ. With the help of bibliometric analysis, most of previous studies have examined DH across multiple dimensions such as research hotspots, co-author networks, and institutional rankings. However, these studies have often been limited in their ability to provide deep insights into the current state of technological advancements and topic development in DH. As a result, their conclusions tend to remain superficial or lack interpretability in understanding how methods and topics interrelate in the field. To address this gap, this study introduced a new concept of Topic-Method Composition (TMC), which refers to a hybrid knowledge structure generated by the co-occurrence of specific research topics and the corresponding method. Especially by analyzing the interaction between TMCs, we can see more clearly the intersection and integration of digital technology and humanistic subjects in DH. Moreover, this study developed a TMC-based workflow combining bibliometric analysis, topic modeling, and network analysis to analyze the development characteristics and patterns of research disciplines. By applying this workflow to large-scale bibliometric data, it enables a detailed view of the knowledge structures, providing a tool adaptable to other fields.

Can ChatGPT predict article retraction based on Twitter mentions?

Detecting problematic research articles timely is a vital task. This study explores whether Twitter mentions of retracted articles can signal potential problems with the articles prior to retraction, thereby playing a role in predicting future retraction of problematic articles. A dataset comprising 3,505 retracted articles and their associated Twitter mentions is analyzed, alongside 3,505 non-retracted articles with similar characteristics obtained using the Coarsened Exact Matching method. The effectiveness of Twitter mentions in predicting article retraction is evaluated by four prediction methods, including manual labelling, keyword identification, machine learning models, and ChatGPT. Manual labelling results indicate that there are indeed retracted articles with their Twitter mentions containing recognizable evidence signaling problems before retraction, although they represent only a limited share of all retracted articles with Twitter mention data (approximately 16%). Using the manual labelling results as the baseline, ChatGPT demonstrates superior performance compared to other methods, implying its potential in assisting human judgment for predicting article retraction. This study uncovers both the potential and limitation of social media events as an early warning system for article retraction, shedding light on a potential application of generative artificial intelligence in promoting research integrity.