论文标题
隐性众包以识别在线社交网络中的虐待行为
Implicit Crowdsourcing for Identifying Abusive Behavior in Online Social Networks
论文作者
论文摘要
在线社交网络传播信息的越来越多的用法是滥用网络欺凌，网络犯罪，垃圾邮件，故意破坏等方面的互联网。为了主动确定网络中的虐待，我们提出了一个模型，以通过众包来识别虐待帖子。通过简单地观察遇到消息的用户之间的自然相互作用，隐式实现了检测机制的众包部分。我们在Twitter上探索信息的节点到节点的传播，并提出了一个模型，该模型通过观察消息的属性以及与之交互的用户来预测与推文相关的滥用水平（滥用，仇恨，垃圾邮件，正常）。我们证明，在识别不同滥用水平的帖子中，可以利用用户与虐待帖子的互动差异。
The increased use of online social networks for the dissemination of information comes with the misuse of the internet for cyberbullying, cybercrime, spam, vandalism, amongst other things. To proactively identify abuse in the networks, we propose a model to identify abusive posts by crowdsourcing. The crowdsourcing part of the detection mechanism is implemented implicitly, by simply observing the natural interaction between users encountering the messages. We explore the node-to-node spread of information on Twitter and propose a model that predicts the abuse level (abusive, hate, spam, normal) associated with the tweet by observing the attributes of the message, along with those of the users interacting with it. We demonstrate that the difference in users' interactions with abusive posts can be leveraged in identifying posts of varying abuse levels.
