Social Media Cyberbullying Detection using Machine Learning
Social Media Cyberbullying Detection using Machine Learning
复制标题
使用机器学习检测社交媒体网络欺凌
DOI：
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复制
发表时间：
2019
影响因子：
0.9
通讯作者：
Ammar Mohammed
中科院分区：
文献类型：
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作者：
John Hani;M. Nashaat;Mostafa Ahmed;Zeyad Emad;Eslam Amer;Ammar Mohammed
随着社交媒体用户的指数增长，网络欺凌已经成为一种通过电子信息进行欺凌的形式。社交网络为欺凌者提供了一个丰富的环境，他们利用这些网络对受害者进行攻击。鉴于网络欺凌对受害者的后果，有必要找到合适的行动来检测和预防它。机器学习可以帮助检测欺凌者的语言模式，从而可以生成一个模型来自动检测网络欺凌行为。本文提出了一种有监督的机器学习方法来检测和预防网络欺凌。几个分类器用于训练和识别欺凌行为。在网络欺凌数据集上的评估表明，神经网络表现更好，准确率达到92.8%，SVM达到90.3。此外，NN在相同数据集上的性能优于其他类似工作的分类器。
With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Social networks provides a rich environment for bullies to uses these networks as vulnerable to attacks against victims. Given the consequences of cyberbullying on victims, it is necessary to find suitable actions to detect and prevent it. Machine learning can be helpful to detect language patterns of the bullies and hence can generate a model to automatically detect cyberbullying actions. This paper proposes a supervised machine learning approach for detecting and preventing cyberbullying. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed approach on cyberbullying dataset shows that Neural Network performs better and achieves accuracy of 92.8% and SVM achieves 90.3. Also, NN outperforms other classifiers of similar work on the same dataset.