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This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Recently, analyses of the characteristics of viral content in the social media field have attracted considerable attention. However, the influence of instant videos has grown significantly, and most social media platforms have begun to introduce them.

The authors conducted a series of independent-samples t-tests using a large-scale data set collected from the YouTube Shorts platform to identify the characteristics of popular instant videos and discussions surrounding them. The authors further analyzed how they differ from other viral content.

The results indicate that viewers leave varied variety of comments based on the topic of conversation in the community, rather than on the video itself. Furthermore, video producers and viewers attempt to reach a consensus in a straightforward and intuitive manner. All analyzed texts contained appropriate attitudes and tendencies according to their roles on the platform.

This study aimed to discover and understand the video and conversational characteristics of popular instant videos, which differ from the existing widely known viral content.