With the advances of Information Communication Technology (ICT) and the popularity of intelligent terminals, Online Social Network, which is characterized by powerful functions of information publishing, dissemination, acquisition and sharing, has attracted a huge number of users and become one of the most popular internet application services currently. However, the growth of Online Social Network has also led to the emergence of cyberbullying issues. Information spreads extremely fast via Online Social Network, making the harm caused by cyberbullying grow exponentially with time. As a result, it becomes critical to detect the cyberbullying in a quick and efficient way. In this paper, in order to solve this challenge, we propose an improved TF-IDF based fastText (ITFT) model for effective cyberbullying detection. Specifically, in our proposed scheme, we improve the TF-IDF algorithm by adding the position weight, keywords are extracted by the improved algorithm and used as input to achieve the purpose of filtering noise data to improve the accuracy. We use the fastText to construct a binary classifier to categorize the input data. Extensive experiments are conducted, and the results demonstrate that our proposed scheme can achieve better efficiency and accuracy in cyberbullying detection as compared with baselines.

中文翻译：

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致力于在线社交网络中高效有效的欺凌检测

随着信息通信技术（ICT）的发展和智能终端的普及，以信息发布，发布，获取和共享的强大功能为特征的在线社交网络吸引了众多用户，成为最庞大的用户之一。当前流行的互联网应用服务。但是，在线社交网络的增长也导致了网络欺凌问题的出现。信息通过在线社交网络迅速传播，使得网络欺凌造成的危害随着时间的增长呈指数级增长。结果，以快速有效的方式检测网络欺凌就变得至关重要。在本文中，为了解决这一挑战，我们提出了一种改进的基于TF-IDF的fastText（ITFT）模型，用于有效的网络欺凌检测。特别，在我们提出的方案中，我们通过增加位置权重来改进TF-IDF算法，改进后的算法提取关键词作为输入，以达到过滤噪声数据提高精度的目的。我们使用fastText构造一个二进制分类器来对输入数据进行分类。进行了广泛的实验，结果表明，与基线相比，我们提出的方案可以在网络欺凌检测中实现更好的效率和准确性。