An adaptive opinion guiding model for online social networks

被引：0

作者：

Xu W.-W. ^{[1
]}

Shi P. ^{[2
]}

Yu L.-B. ^{[3
]}

Hu C.-J. ^{[1
]}

机构：

[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing

[2] National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing

[3] The Micro Dreaming of Beijing Network Technology Company Limited, Beijing

来源：

Shi, Peng (shipengustb@sina.com) | 1714年 / Chinese Institute of Electronics卷 / 44期

关键词：

Content-based recommendation; Online social networks; Opinion guiding; Sentiment analysis;

D O I：

10.3969/j.issn.0372-2112.2016.07.028

中图分类号：

学科分类号：

摘要：

Online social networks are now recognized as an important platform for the spread of information. While providing convenient exchange for users, it also makes OSNs fertile grounds for the wide spread of misinformation which can lead to undesirable consequences. Most mainstream media outlets use keyword matching as a search method to find misinformation and forbid the presentation in context. However, this method also blocks positive messages related to misinformation. In this paper, we propose an adaptive opinion guiding model to limit the spread of misinformation. Based on user's opinion and sentiment, the model recommends messages or other users that have relative positive feeling to current user. It also introduces the feedback mechanism to achieve a long-term and accurate guiding by adjusting the pushing content dynamically. We also design and finish the guiding system. Experiments show that the model can guide the opinion of the network group effectively. © 2016, Chinese Institute of Electronics. All right reserved.

引用

页码：1714 / 1720

页数：6

共 22 条

[21] Liu S.-Z., Liao Z.-F., Hu J., Et al., Classified time homogeneous Markov model for recommendation based on implicit feedback, Acta Electronica Sinica, 42, 4, pp. 703-710, (2014)
[22] Yang S.H., Long B., Smola A., Et al., Like like alike: joint friendship and interest propagation in social networks, Proceedings of the 20th International Conference on World Wide Web, pp. 537-546, (2011)

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