论文标题
一个自举模型，以检测白人至上主义语料库中的滥用和意图
A Bootstrapped Model to Detect Abuse and Intent in White Supremacist Corpora
论文作者
论文摘要
情报分析师面临一个困难的问题：将极端主义的言论与潜在的极端主义暴力区分开来。许多人满足于对某些目标群体表达虐待，但只有少数表明愿意从事暴力行为。我们通过建立意图的预测模型，从一组意图单词的种子和表达意图的语言模板来解决这个问题。我们为意图设计了一个基于N-Gram和基于注意力的深层学习者，并将其用作Colearner，以改善预测和预测本身的基础。它们在几轮中收敛于稳定的预测。我们将意图的预测与滥用语言的预测合并，以检测表明对暴力行动渴望的帖子。我们通过将预测与众包标签进行比较来验证预测。该方法可以应用于可以定义合理起点的其他语言特性。
Intelligence analysts face a difficult problem: distinguishing extremist rhetoric from potential extremist violence. Many are content to express abuse against some target group, but only a few indicate a willingness to engage in violence. We address this problem by building a predictive model for intent, bootstrapping from a seed set of intent words, and language templates expressing intent. We design both an n-gram and attention-based deep learner for intent and use them as colearners to improve both the basis for prediction and the predictions themselves. They converge to stable predictions in a few rounds. We merge predictions of intent with predictions of abusive language to detect posts that indicate a desire for violent action. We validate the predictions by comparing them to crowd-sourced labelling. The methodology can be applied to other linguistic properties for which a plausible starting point can be defined.
