A Bootstrapped Approach for Abusive Intent Detection in Social Media Content
The proliferation of Internet connected devices continues to result in the creation of massive collections of human generated content from websites such as social media. Unfortunately, some of these sites are used by criminal or terrorist organizations for recruitment or to spread rhetoric. By analyzing this content, it is possible to gain insights into the future actions of the writers. This information can support organizations in taking proactive measures to modify or stop said actions from taking place. The textual feature of interest is the expression of abusive intent, which can be thought of as a plan to carry out a malicious action. The proposed approach independently detects abuse and intent in documents, then computes a joint prediction for the document. Abusive language detection is a well-studied problem, which enabled a model to be trained using supervised learning. The intent detection model requires a semi-supervised technique since no labelled datasets exist. To do this, an initial collection of labels was generated using a linguistic model. These labels were then used to co-train a statistical and deep learning model. Using crowd-sourced labels, the abuse and intent models were found to have accuracies of 95% and 80%, respectively. The joint predictions were then used to prioritize documents for manual assessment.
URI for this recordhttp://hdl.handle.net/1974/28123
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