滑向极端：一种解释极端观点如何渗透在线讨论的混合方法
Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions
摘要：
定性研究为观察和研究在线社交媒体平台上的社区和文化提供了方法论指导。然而，这种方法需要研究人员付出相当大的手工努力，并且可能过于集中并局限于某些在线群体。这项工作提出了一个完整的解决方案，以加速有问题的在线语音的定性分析，重点是意见出现在网上社区利用机器学习算法。首先，我们采用深入观察的定性方法来理解有问题的在线语音。这一初步的定性研究构建了一个有问题的言论的本体论，其中包含了社会媒体上的帖子与他们的基本意见注释。定性研究动态构建的意见集，同时标记的帖子。接下来，我们使用关键字从三个在线社交媒体平台（Facebook，Twitter和Youtube）收集大型数据集。最后，我们引入一个迭代数据探索过程来增强数据集。它在数据采样器（平衡未标记数据的探索和利用）、采样数据的自动标记、定性映射团队的手动检查以及自动意见分类器的再培训之间交替。我们提出了定性和定量的结果。首先，我们证明了我们的人在循环方法成功地增强了初始的定性标记和狭隘的集中数据集，并构建了一个更全面的数据集。接下来，我们提出了详细的案例研究的动态有问题的言论在一个极右的Facebook组，举例说明其从保守到极端的突变。最后，我们研究了意见出现和共现的动态，我们暗示了一些途径，通过这些途径，极端意见悄悄进入主流在线话语。
英文摘要：
Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narrowed to certain online groups. This work proposes a complete solution to accelerate the qualitative analysis of problematic online speech, focusing on opinions emerging from online communities by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we use keywords to collect a large dataset from three online social media platforms (Facebook, Twitter, and Youtube). Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler -- which balances exploration and exploitation of unlabeled data -- the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team, and, finally, the retraining of the automatic opinion classifiers. We present both qualitative and quantitative results. First, we show that our human-in-the-loop method successfully augments the initial qualitatively labeled and narrowly focused dataset and constructs a more encompassing dataset. Next, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Finally, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some pathways through which extreme opinions creep into the mainstream online discourse.
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