A Herd Effect Detection Method Based on Text Features

Abstract

The herd effect is a common phenomenon in social society. The detection of this phenomenon is of great significance in many tasks based on social network analysis such as recommendation. However, the research on social network and natural language processing seldom focuses on this issue. In this paper, we propose an unsupervised data mining method to detect herding in social networks. Taking shopping review as an example, our algorithm can identify other reviews which are affected by some previous reviews and detect a herd effect chain. From the overall perspective, the cross effects of all views form the herd effect graph. This algorithm can be widely used in various social network analysis methods through graph structure, which provides new useful features for many tasks.

SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution

“…In recent years, vision transformers (ViTs) [1] have gradually surpassed and replaced Convolution Neural Network (CNN) and found wide applications in various downstream tasks of medical imaging, including segmentation [2] , [3] , [4] , [5] , classification [6] , [7] , [8] , [9] , restoration [10] , [11] , [12] , [13] , synthesis [14] , [15] , [16] , [17] , registration [18] , [19] , [20] , [21] , and object detection in medical images [22] , [23] . In particular, significant progress has been observed in 3D medical image segmentation with the adoption of Vision Transformers (ViTs) [24] , [25] , [26] , [27] , [28] .…”