Abstract
In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.
Highlights
In recent years, with the rapid development of internet technology, social networks have played an increasingly important role in people’s lives [1]
Machine learning algorithms have made significant developments in many fields, it cannot be ignored that machine learning algorithms are vulnerable to attacks from adversarial examples
We review the application of machine learning algorithms in the field of social networks from aspects of sentiment analysis, recommendation systems, and spam detection, as well as the research progress of the generation of adversarial examples and defense algorithms in social networks
Summary
With the rapid development of internet technology, social networks have played an increasingly important role in people’s lives [1]. More and more people are willing to share new things happening around them with friends through social networks, and government agencies release the latest policy information to the public through social networks. With the rapid popularization of social networks, the role of social networks is not limited to providing people with a channel to communicate with friends. Users can be profiled according to its timelines, the system can recommend friends, topics, information, and products that users may be interested in, which can greatly enrich people’s leisure life. Research on social network information dissemination [2, 3] can facilitate social network marketing and effectively predict and control public opinion. How to use social networks to achieve various functions has become a research hotspot in recent years
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