分析推特用户激进化的风险指标，发现关键词表现优于习惯用语。
Statistical Analysis of Risk Assessment Factors and Metrics to Evaluate Radicalisation in Twitter
- 用关键词匹配情绪和立场，评估激进风险
- 检测歧视感与对西方及圣战态度的指标有效
- 频繁使用省略号等习惯特征效果不佳
如今，社交媒体已成为重要的沟通工具，产生大量关于用户及其互动的信息，可通过数据挖掘方法进行分析。近年来，社交媒体被用于煽动极端化。本文研究了一组指标及其度量在三个不同数据集上评估个体激进化风险的表现。基于关键词的度量，尽管受语言影响，但在衡量挫折感、感知歧视以及对西方社会和圣战的正负态度方面表现良好。然而，基于常见习惯（如使用省略号）的度量不足以有效刻画处于激进化风险中的用户。论文详细描述了用于评估社交媒体激进化的指标集合及所用数据集，并在这些数据集上开展实验，评估所考虑度量的性能。
原文摘要 · Abstract (English)
Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to asses the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered.
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