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In the present world of constant connectivity, the barrage system, as a system of real-time dynamic comments coupled with video content, has become a popular interactive system technology for video sharing platforms. This study investigates how barrage system fluctuation characteristics, namely, barrage fluctuation amplitude and frequency, impact user interaction.

The research model was estimated with a fixed-effects regression applied to a longitudinal panel dataset collected from one of the most popular video sharing platforms in China (Bilibili.com).

Barrage fluctuation frequency has positive effects on users' real-time (synchronous) barrage interaction and the traditional (asynchronous) comment interaction. Barrage fluctuation amplitude has a positive effect on users' real-time (synchronous) barrage interaction but a negative effect on traditional (asynchronous) comment interaction. In addition, the interaction effects of the barrage fluctuation frequency and the barrage fluctuation amplitude on user interaction show adverse effects.

The results revealed the impact of different barrage fluctuation characteristics on different forms of interaction and provide important theoretical contributions and managerial implications in terms of user interaction on video sharing platforms.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Financial market‐related crimes seem to continually increase in number as well as in the amount of illicit profits. This emerging situation has obliged governments and self‐regulated bodies to act aggressively on the issue. This paper provides a snapshot of the evolution timeline of financial crimes and discussion in support of the fight against this plague.

Based on financial crime literature and field work.

Improvement in the expertise and degree of refinement employed by both organized crime and criminal businessmen.

Some information originates from confidential sources and consequently could not be further developed.

Contemporary picture of the current situation. Some recommendations were submitted to regulatory authorities who are examining and adjusting their actions accordingly.

This paper explores the “proto-Keynesian” ideas of progressive members of the scientific management community with regard to micro- and macroeconomic planning/management.

Based on a systematic exegetical analysis of articles published in a largely unexplored primary/archival source, the Bulletin of the Taylor Society between 1915 and 1934.

This paper surfaces a latent “proto-Keynesian” bedrock among progressive segments of the US management community that provides a more cogent explanation for the wholehearted reception, as well as the decisive impact, of Keynes’ ideas on US macroeconomic policy than do extant explanations in the history of economic thought. Further, it reveals that most of these progressive managers with views as to both cause of and solution for the 1930’s Depression were members of the Taylor Society, an epistemic community devoted to the ideas of Frederick Winslow Taylor, the father of scientific management.

The paper adds to the small but growing corpus of revisionist management history that seeks to problematize the received wisdom about scientific management or Taylorism. Few, if any, management historians appreciate that F. W. Taylor provided the basic planning tools which if developed, could enhance humanity’s control over anarchic market forces and aid the construction of a society based on democratic and effective planning.