Identifying Differentiating Factors for Cyberbullying in Vine and Instagram
Identifying Differentiating Factors for Cyberbullying in Vine and Instagram
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识别 Vine 和 Instagram 网络欺凌的差异化因素
DOI：
10.1007/978-3-030-76228-5_25
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Mishra, S.
中科院分区：
文献类型：
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作者：
Rafiq, R.;Hosseinmardi, H.;Han, R.;Lv, Q.;Mishra, S.
在过去的十年中，出现了大量不同类型的在线社交网络（osn）。由于它们的广泛普及和持续可用性，通过这些osn发起的网络欺凌威胁达到了前所未有的水平。网络欺凌的受害者现在比以往任何时候都更容易受到捕食者、肇事者和跟踪者的攻击。在这项工作中，我们通过使用两个标记数据集，对Vine和Instagram社交网络上的用户帖子进行了详细的分析。这些帖子包括媒体帖子和用户评论，这些帖子被标记为网络欺凌实例或不是。我们的分析揭示了这些社交网络中网络欺凌和非网络欺凌实例之间的几个重要区别因素。特别是，网络欺凌和非网络欺凌实例在以下方面存在差异：(i)唯一负面评论的数量，（ii）积极和消极情绪评论的时间分布，以及（iii）媒体标题和后续评论的文本内容。这些分析的结果可以用来建立高度准确的分类器来识别网络欺凌实例。
A multitude of online social networks (OSNs) of varying types has been introduced in the past decade. Because of their enormous popularity and constant availability, the threat of cyberbullying launched via these OSNs has reached an unprecedented level. Victims of cyberbullying are now more vulnerable than ever before to the predators, perpetrators, and stalkers. In this work, we perform a detailed analysis of user postings on Vine and Instagram social networks by making use of two labeled datasets. These postings include threads of media posts and user comments that were labeled for being cyberbullying instances or not. Our analysis has revealed several important differentiating factors between cyberbullying and non-cyberbullying instances in these social networks. In particular, cyberbullying and non-cyberbullying instances differ in (i) the number of unique negative commenters, (ii) temporal distribution of positive and negative sentiment comments, and (iii) textual content of media captions and subsequent comments. The results of these analyses can be used to build highly accurate classifiers for identifying cyberbullying instances.
社交网络中网络欺凌检测的半监督学习
DOI：
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发表时间：
2014
期刊：
Australasian Database Conference
影响因子：
--
作者：
V. Nahar;S. Al;Xue Li;C. Pang
通讯作者：
C. Pang