Liu T, Zhou T, Shi Y, et al. A Herd Effect Detection Method Based on Text Features[C] // Proceedings of 2022 International Conference on Natural Language Processing. Xian: IEEE, 2022, DOI:10.20944/preprints202111.0499.v1.
Liu T, Zhou T, Shi Y, et al. A Herd Effect Detection Method Based on Text Features[C] // Proceedings of 2022 International Conference on Natural Language Processing. Xian: IEEE, 2022, DOI:10.20944/preprints202111.0499.v1.
Liu T, Zhou T, Shi Y, et al. A Herd Effect Detection Method Based on Text Features[C] // Proceedings of 2022 International Conference on Natural Language Processing. Xian: IEEE, 2022, DOI:10.20944/preprints202111.0499.v1.
Liu T, Zhou T, Shi Y, et al. A Herd Effect Detection Method Based on Text Features[C] // Proceedings of 2022 International Conference on Natural Language Processing. Xian: IEEE, 2022, 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.
Social Networks; Data Mining; Graph Structure; Natural Language Processing; Machine Learning
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
