Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Herd Effect Detection Method Based on Text Features

Version 1 : Received: 25 November 2021 / Approved: 26 November 2021 / Online: 26 November 2021 (10:45:06 CET)

How to cite: Liu, T.; Zhou, T.; Shi, Y.; Liu, S.; Gao, J. A Herd Effect Detection Method Based on Text Features. Preprints 2021, 2021110499 (doi: 10.20944/preprints202111.0499.v1). Liu, T.; Zhou, T.; Shi, Y.; Liu, S.; Gao, J. A Herd Effect Detection Method Based on Text Features. Preprints 2021, 2021110499 (doi: 10.20944/preprints202111.0499.v1).

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.

Supplementary and Associated Material

Keywords

Social Networks; Data Mining; Graph Structure; Natural Language Processing; Machine Learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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