2022/06/20 by Mark Anderson, David E. Millard · 1 citation
Decision Sciences · Computer Science · #Scientific Computing and Data Management #scientometrics and bibliometrics research #Data Visualization and Analytics
paper · doi:10.1145/3511095.3531271
openalex publication_date 2022/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Conferences such as ACM Hypertext have been running for many decades and the metadata on their collected publications represent a valuable scholarly meta-history on areas such as the community’s health, diversity, and changing interests. But the metadata about these papers is not readily available for analysis, and the data collection and cleaning tasks appear substantial. In this paper we attempt to explore this challenge using the ACM Hypertext series as a case study. Taking the ACM Digital Library as a starting point, and using a combination of manual and automatic methods, we have constructed and released a 3-star Open Dataset representing over 1000 publications by almost 2,500 authors. An initial analysis reveals a modestly-sized but robust conference, with a changing pattern of in-citations that co-occurs with the arrival of social media, and a relatively consistent but imbalanced gender ratio of authors that shows some signs of recent improvements. The challenges encountered included identifying discrete author names, potential issues with text retrieval from PDF, and a disparate set of author keywords that reveals an absence of a common vocabulary. These insights are the results of a hard-fought process that is made complex by an incomplete digital record and a lack of consistency in naming. This Hypertext case study thus reveals a serious shortfall in the way that scholarly activity is captured and described, and questions PDF as the primary method of recording publications. Addressing these issues would make further analysis more straightforward and would allow larger events (with orders of magnitude more data) to be analysed in a similar way.