Productivity in the Internet mailing lists: A bibliometric analysis

AbstractThe author examines patterns of productivity in the Internet mailing lists, also known as discussion lists or discussion groups. Datasets have been collected from electronic archives of two Internet mailing lists, the LINGUIST and the History of the English Language. Theoretical models widely used in informetric research have been applied to fit the distribution of posted messages over the population of authors. The Generalized Inverse Poisson‐Gaussian and Poisson‐lognormal distributions show excellent results in both datasets, while Lotka and Yule–Simon distribution demonstrate poor‐to‐mediocre fits. In the mailing list where moderation and quality control are enforced to a higher degree, i.e., the LINGUIST, Lotka, and Yule–Simon distributions perform better. The findings can be plausibly explained by the lesser applicability of the success‐breeds‐success model to the information production in the electronic communication media, such as Internet mailing lists, where selectivity of publications is marginal or nonexistent. The hypothesis is preliminary, and needs to be validated against the larger variety of datasets. Characteristics of the quality control, competitiveness, and the reward structure in Internet mailing lists as compared to professional scholarly journals are discussed.

Productivity in the Internet mailing lists: A bibliometric analysis Journal Articles