基于神经网络方法的网络欺凌识别

Ekaterina Sergeevna Momentum, Andrei Viktorovich Filimonov, A. V. Osipov, S. T. Gataullin
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引用次数: 0

摘要

作者详细考虑了网络欺凌的识别,这是由欺诈者非法使用受害者的个人数据进行的。基本上,这些信息的来源是社交网络,电子邮件。社交网络在社会中的使用每天都呈指数级增长。使用社交网络,除了众多的优势,也有一个负面的特点,即用户面临众多的网络威胁。此类威胁包括将个人数据用于犯罪目的、网络欺凌、网络犯罪、网络钓鱼和网络欺凌。在本文中,我们将重点关注识别喷子的任务。识别社交网络上的喷子是一项艰巨的任务,因为它们本质上是动态的,并且收集了数十亿条记录。识别巨魔的一个可能的解决方案是使用机器学习算法。作者对该主题研究的主要贡献是使用了识别社交网络中的喷子的方法,该方法基于对网络用户的情绪状态和行为活动的分析。在本文中,为了识别喷子,将用户分组在一起,这种关联是通过识别类似的通信方式来实现的。用户的分布是通过使用一种特殊类型的神经网络自动进行的,即自组织Kohonen地图。群组号码也会自动确定。为了确定用户的特征,在此基础上进行分组分布,使用了评论数,评论的平均长度和负责用户情绪状态的指标。
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Identification of cyberbullying by neural network methods
The authors consider in detail the identification of cyberbullying, which is carried out by fraudsters with the illegal use of the victim's personal data. Basically, the source of this information is social networks, e-mails. The use of social networks in society is growing exponentially on a daily basis. The use of social networks, in addition to numerous advantages, also has a negative character, namely, users face numerous cyber threats. Such threats include the use of personal data for criminal purposes, cyberbullying, cybercrime, phishing and cyberbullying. In this article, we will focus on the task of identifying trolls. Identifying trolls on social networks is a difficult task because they are dynamic in nature and are collected in several billion records. One of the possible solutions to identify trolls is the use of machine learning algorithms. The main contribution of the authors to the study of the topic is the use of the method of identifying trolls in social networks, which is based on the analysis of the emotional state of network users and behavioral activity. In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication. The distribution of users is carried out automatically through the use of a special type of neural networks, namely self-organizing Kohonen maps. The group number is also determined automatically. To determine the characteristics of users, on the basis of which the distribution into groups takes place, the number of comments, the average length of the comment and the indicator responsible for the emotional state of the user are used.
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