Cyberbullying detection using probabilistic socio-textual information fusion
Cyberbullying detection using probabilistic socio-textual information fusion
复制标题
使用概率社会文本信息融合检测网络欺凌
DOI：
10.1109/asonam.2016.7752342
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Atrey
中科院分区：
文献类型：
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作者：
Vivek K. Singh;Qianjia Huang;P. Atrey
网络欺凌是在线社交网络（OSN）中一个重要的社会技术挑战。随着OSN中异构数据的增长趋势、更好的网络表征和文本特征的复杂性，最近的努力已经意识到查看异构信息模式（包括文本特征、社交特征和基于图像的特征）对更好地检测网络欺凌的价值。然而，这些方法仍然单独或“天真地”使用这些特征，而没有考虑与每个特征相关的不同置信度或特征之间的相互依赖性。我们提出了一种新的概率信息融合框架，该框架利用与不同社会和文本特征相关的置信度评分和相互依赖性，并利用这些来构建更好的网络欺凌预测因子。将所提出的方法的性能与文献中最近使用类似数据集和特征的方法进行了比较，所提出的方法在网络欺凌检测方面取得了显着改进。
Cyberbullying is an important socio-technical challenge in Online Social Networks (OSN). With the growth trends of heterogeneous data in OSN, better network characterization, and textual feature sophistication, recent efforts have realized the value of looking at heterogeneous modes of information including textual features, social features, and image-based features for better cyberbullying detection. These approaches, however, still use these features either individually or combine them `naively' without considering the different confidence levels associated with each feature or the interdependencies between features. We propose a novel probabilistic information fusion framework that utilizes confidence score and interdependencies associated with different social and textual features and uses those to build better predictors for cyberbullying. The performance of the proposed approach was compared to a recent approach in literature which used a similar dataset and features and the proposed approach resulted in significant improvements in terms of cyberbullying detection.