With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.

中文翻译：

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基于 FastText 和单词相似性方案的网络欺凌检测

随着在线社交网络 (OSN) 的最新发展，这些服务在日常生活中得到广泛应用。另一方面，网络欺凌是一种相对较新的通过基于互联网的电子设备进行的骚扰，在在线社交网络中正在兴起。因此，学者们被吸引到调查网络欺凌行为。研究表明，网络欺凌对心理健康具有破坏性影响，尤其是对青少年而言。为了减少甚至停止网络欺凌，应用了不同的机器学习技术，迄今为止已经进行了大量研究。然而，传统的检测方案仍然存在挑战，例如精度低。因此，在自然语言处理和机器学习社区中找到有效的检测解决方案具有重要意义。在目前的研究中，首先从词汇和句法的角度分析网络欺凌的特征。然后提出了一种基于FastText和单词相似度方案的新检测算法。最后，通过实验来评估所提出方法的有效性和性能。所得结果表明，该算法能有效提高网络欺凌检测的检测准确率和召回率。