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Identifying Right-Wing Extremism in German Twitter Profiles: A Classification Approach

机译:在德国Twitter个人资料中识别右翼极端主义:一种分类方法

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Social media platforms are used by an increasing number of extremist political actors for mobilization, recruiting or radicalization purposes. We propose a machine learning approach to support manual monitoring aiming at identifying right-wing extremist content in German Twitter profiles. We frame the task as profile classification, based on textual cues, traits of emotionality in language use, and linguistic patterns. A quantitative evaluation reveals a limited precision of 25% with a close-to-perfect recall of 95%. This leads to a considerable reduction of the workload of human analysts in detecting right-wing extremist users.
机译:越来越多的极端政治人物使用社交媒体平台进行动员,招募或激进化。我们提出了一种机器学习方法来支持手动监视,旨在识别德国Twitter个人资料中的右翼极端主义内容。我们根据文字提示,语言使用中的情绪特征和语言模式,将任务划分为个人资料分类。定量评估显示有限的精度为25%,接近完美的召回率为95%。这大大减少了人类分析人员在检测右翼极端主义用户方面的工作量。

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