Toward efficient and effective bullying detection in online social network
Toward efficient and effective bullying detection in online social network
复制标题
在线社交网络中高效且有效的欺凌检测
DOI：
10.1007/s12083-019-00832-1
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发表时间：
2020
影响因子：
4.2
通讯作者：
Li Jinguo
中科院分区：
文献类型：
--
作者：
Wu Jiale;Wen Mi;Lu Rongxing;Li Beibei;Li Jinguo
随着信息通信技术的进步和智能终端的普及，在线社交网络以其强大的信息发布、传播、获取和分享功能吸引了大量用户，成为当前最热门的互联网应用服务之一。然而，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传播得非常快，使得网络欺凌造成的危害随着时间呈指数级增长。因此，以快速有效的方式检测网络欺凌变得至关重要。在本文中，为了解决这一挑战，我们提出了一种改进的TF-IDF为基础的快速文本（ITFT）模型有效的网络欺凌检测。具体来说，在我们提出的方案中，通过增加位置权重对TF-IDF算法进行改进，利用改进后的算法提取关键词作为输入，达到过滤噪声数据提高准确率的目的。我们使用fastText构造一个二元分类器来对输入数据进行分类。大量的实验进行，结果表明，我们提出的方案可以实现更好的效率和准确性相比，基线的网络欺凌检测。
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.