使用情境维度建模社交媒体上的伊斯兰极端主义传播：宗教、意识形态与仇恨
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
AI总结：
本研究提出一种基于宗教、意识形态和仇恨三个情境维度的计算方法，用于分析Twitter上的伊斯兰极端主义内容，在精确度上比基线高10.2%，并有助于理解在线激进化过程。
摘要：
恐怖袭击在一定程度上与在线极端主义内容相关联。尽管数以万计的伊斯兰极端主义支持者消费此类内容，但与和平的穆斯林相比，他们只占很小一部分。遏制社交媒体平台上不断演变的极端主义的努力仍然不足且大多无效。极端主义与主流情境之间的差异对机器解释构成挑战，尤其威胁到分类算法的精确性。我们提出的情境感知计算方法用于分析Twitter上的极端主义内容，将这一说服过程分解为承认内在模糊性和稀疏性的构建模块，这些模糊性和稀疏性可能同时挑战人工和自动分类。我们使用三个情境维度的组合来建模这一过程——宗教、意识形态和仇恨——每个维度阐明一定程度的激进化，并突出独立特征以使其在计算上可访问。我们为每个情境维度利用领域特定的知识资源，例如宗教使用《古兰经》，政治意识形态使用极端主义意识形态者和传教士的书籍，仇恨使用社交媒体仇恨言论语料库。我们的研究为可靠分析做出三项贡献：（i）开发一种植根于宗教、意识形态和仇恨情境维度的计算方法，反映在线伊斯兰极端主义群体采用的策略，（ii）针对这些维度对相关推文数据集进行深入分析，以排除可能被错误标记的用户，（iii）提供一个将在线激进化理解为过程的框架，以辅助反制规划。鉴于潜在的重大社会影响，我们评估了算法性能以最小化错误标记，我们的方法在精确度上比竞争基线高出10.2%。
英文摘要：
Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.
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